{ "cells": [ { "cell_type": "markdown", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "# Bayesian optimization of a Y branch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note: the cost of running the entire notebook is larger than 10 FlexCredits.**\n", "\n", "[Bayesian optimization](https://en.wikipedia.org/wiki/Bayesian_optimization) is a popular technique for searching and optimizing a design space. It works by building a probabilistic model, such as a [Gaussian process](https://en.wikipedia.org/wiki/Gaussian_process), to predict the outcome of a more complex objective function. Unlike other optimizers like genetic algorithms or particle swarm optimization, Bayesian optimization uses an acquisition function to select new potential solutions, focusing on areas of the design space that the Gaussian process predicts will have high objective values and high uncertainty. Over the course of the optimization, the model's uncertainty reduces, improving its accuracy as a surrogate for the true objective function. This makes Bayesian optimization a direct approach to optimization, particularly well-suited to problems with small (< 30 dimensions) and expensive-to-evaluate objective functions. \n", "\n", "However, as Bayesian optimization is an iterative process with many individual components, it can be challenging to design and parallelize, leading to long run times and high computational costs. Fortunately, the Design plugin in ``Tidy3D`` includes the Bayesian optimization method ``MethodBayOpt`` which eliminates much of this complexity. Users can quickly and easily run a Bayesian optimization for complex FDTD simulations and analyze the results. Further details on Bayesian optimization and the methods used in ``Tidy3D`` are available in the open-source Python library [bayesian_optimization](https://bayesian-optimization.github.io/BayesianOptimization/basic-tour.html).\n", "\n", "This notebook details how to perform a Bayesian optimization with the ``Tidy3D`` Design plugin for the development of a Y-junction. It is based on the work of `Zhengqi Gao, Zhengxing Zhang, and Duane S. Boning, \"Automatic Synthesis of Broadband Silicon Photonic Devices via Bayesian Optimization\" J. Light. Technol. 40, 7879-7892 (2022)` [DOI:10.1109/JLT.2022.3207052](https://doi.org/10.1109/JLT.2022.3207052). A detailed description of how to build a Y-junction can be found in the [Waveguide Y junction](https://www.flexcompute.com/tidy3d/examples/notebooks/YJunction/).\n", "\n", "\n", "\"Schematic\n", "\n", "If you are curious about other `Design` plugin features, see these notebooks:\n", "\n", "1. [Intro to Design Plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/)\n", "\n", "2. [Particle Swarm Optimizer PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n", "\n", "3. [Particle Swarm Optimizer Bullseye Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n", "\n", "4. [Genetic Algorithm Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n", "\n", "5. [All-Dielectric Structural Colors](https://www.flexcompute.com/tidy3d/examples/notebooks/AllDielectricStructuralColor/)\n", "\n", "6. [Parameter Scan](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# The Bayesian optimizer uses the bayesian-optimization external package version 1.5.1.\n", "# Uncomment the following line to install the package\n", "# pip install bayesian-optimization==1.5.1\n", "\n", "import gdstk\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import tidy3d as td\n", "import tidy3d.plugins.design as tdd\n", "import tidy3d.web as web\n", "from scipy.interpolate import make_interp_spline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Simulation Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulation is defined across the 1.5 $\\mu m$ to 1.6 $\\mu m$ wavelength range with 100 sampling points. The base of the model is silicon, the top of the model is silicon dioxide; we use the material constants included in the Tidy3D Tidy3D's [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#)." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "lda0 = 1.55 # central wavelength\n", "freq0 = td.C_0 / lda0 # central frequency\n", "n_wav = 100 # Number of wavelengths to sample in the range\n", "ldas = np.linspace(1.5, 1.6, n_wav) # wavelength range\n", "freqs = td.C_0 / ldas # frequency range\n", "fwidth = 0.5 * (np.max(freqs) - np.min(freqs)) # width of the source frequency range\n", "\n", "si = td.material_library[\"cSi\"][\"Palik_Lossless\"]\n", "sio2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The following parameters describe the geometry of the waveguide. The optimizable design space is the junction which is split into 13 discrete segments." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "t = 0.22 # thickness of the silicon layer\n", "num_d = 13 # dimensional space of the design region\n", "\n", "l_in = 1 # input waveguide length\n", "l_junction = 2 # length of the junction\n", "l_bend = 6 # horizontal length of the waveguide bend\n", "h_bend = 2 # vertical offset of the waveguide bend\n", "l_out = 1 # output waveguide length\n", "branch_width = 0.5 # width of one Y branch\n", "branch_sep = 0.2 # distance between y branches at the junction\n", "inf_eff = 100 # effective infinity" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most effective way to use the Design plugin is to split the workflow into \"pre\" and \"post\" functions which can surround a call to the Tidy3D cloud that carries out the computation. This takes advantage of automated simulation batching, allowing for parallelization which saves a considerable amount of time. The pre-function returns a `Simulation`; the post-function then analyzes the corresponding `SimulationData`. Together they can be considered the fitness function (or objective function / figure of merit) which the Bayesian optimization process is working to predict.\n", "\n", "The function ``fn_pre`` needs to take the parameters that are being optimized: these are the 13 width segments of the junction. They always input as a dictionary and can be unpacked within the function (as below) or included as keyword arguments. These parameters are used to build a Y-junction ``Simulation`` object.\n", "\n", "The function ``fn_post`` then takes the ``SimulationData`` object output by the simulation and computes the objective function. The value of objective is then fed back into the Bayesian optimization to inform the probabilistic model. In this case, we extract the power passing through the Y-junction and the power reflected back to the source. These values are evaluated in a custom loss function described by ``Gao et al.`` to determine the effectiveness of the junction design:\n", "\n", "\\begin{equation}\n", " -\\frac{N_\\lambda}{3} \\sum_\\lambda \\left( R^2 + 2(T-0.5)^2 \\right)\n", "\\end{equation}\n", "where the summation is performed over the simulated wavelengths, $N_\\lambda$ is the number of wavelength points, $R$ is the reflected power and $T$ is the transmitted power in one branch. \n", "\n", "The aim of this function is to achieve a transmitted power of 0.5 through each branch, whilst driving the power reflected towards the source to zero. Note there is a minus sign in front of the output value as this loss function is a minimizing function, whilst all the optimizers in the Design plugin are built to maximize the objective function. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def fn_pre(**w_params: dict) -> td.Simulation:\n", " \"\"\"Create a Simulation of a Y splitter from a series of junction widths.\n", "\n", " Includes mode monitors to measure the power transmitted and reflected to source.\n", " \"\"\"\n", " w_start = 0.5\n", " w_end = branch_width * 2 + branch_sep\n", "\n", " widths = [w_start] # Ensures input waveguide is included in spline for first point of junction\n", " widths.extend(list(w_params.values()))\n", " widths.append(w_end) # Ensures final point of junction smoothly converts to the branches\n", "\n", " x_junction = np.linspace(\n", " l_in, l_in + l_junction, num_d + 2\n", " ) # x coordinates of the top edge vertices\n", " y_junction = np.array(widths) # y coordinates of the top edge vertices\n", "\n", " # pass vertices through spline and increase sampling to smooth the geometry\n", " new_x_junction = np.linspace(\n", " l_in, l_in + l_junction, 100\n", " ) # x coordinates of the top edge vertices\n", " spline = make_interp_spline(x_junction, y_junction, k=2)\n", " spline_yjunction = spline(new_x_junction)\n", "\n", " # using concatenate to include bottom edge vertices\n", " x_junction = np.concatenate((new_x_junction, np.flipud(new_x_junction)))\n", " y_junction = np.concatenate((spline_yjunction / 2, -np.flipud(spline_yjunction / 2)))\n", "\n", " # stacking x and y coordinates to form vertices pairs\n", " vertices = np.transpose(np.vstack((x_junction, y_junction)))\n", "\n", " junction = td.Structure(\n", " geometry=td.PolySlab(vertices=vertices, axis=2, slab_bounds=(0, t)), medium=si\n", " )\n", "\n", " x_start = l_in + l_junction # x coordinate of the starting point of the waveguide bends\n", "\n", " x_bend = np.linspace(x_start, x_start + l_bend, 100) # x coordinates of the top edge vertices\n", "\n", " y_bend = (\n", " (x_bend - x_start) * h_bend / l_bend\n", " - h_bend * np.sin(2 * np.pi * (x_bend - x_start) / l_bend) / (np.pi * 2)\n", " + w_end / 2\n", " - w_start / 2\n", " ) # y coordinates of the top edge vertices\n", "\n", " # adding the last point to include the straight waveguide at the output\n", " x_bend = np.append(x_bend, inf_eff)\n", " y_bend = np.append(y_bend, y_bend[-1])\n", "\n", " # add path to the cell\n", " cell = gdstk.Cell(\"bends\")\n", " cell.add(\n", " gdstk.FlexPath(x_bend + 1j * y_bend, branch_width, layer=1, datatype=0)\n", " ) # top waveguide bend\n", " cell.add(\n", " gdstk.FlexPath(x_bend - 1j * y_bend, branch_width, layer=1, datatype=0)\n", " ) # bottom waveguide bend\n", "\n", " # define top waveguide bend structure\n", " wg_bend_1 = td.Structure(\n", " geometry=td.PolySlab.from_gds(\n", " cell,\n", " gds_layer=1,\n", " axis=2,\n", " slab_bounds=(0, t),\n", " )[0],\n", " medium=si,\n", " )\n", "\n", " # define bottom waveguide bend structure\n", " wg_bend_2 = td.Structure(\n", " geometry=td.PolySlab.from_gds(\n", " cell,\n", " gds_layer=1,\n", " axis=2,\n", " slab_bounds=(0, t),\n", " )[1],\n", " medium=si,\n", " )\n", "\n", " # straight input waveguide\n", " wg_in = td.Structure(\n", " geometry=td.Box.from_bounds(rmin=(-inf_eff, -w_start / 2, 0), rmax=(l_in, w_start / 2, t)),\n", " medium=si,\n", " )\n", "\n", " # the entire model is the collection of all structures defined so far\n", " model_structure = [wg_in, junction, wg_bend_1, wg_bend_2]\n", "\n", " Lx = l_in + l_junction + l_out + l_bend # simulation domain size in x direction\n", " Ly = w_end + 2 * h_bend + 1.5 * lda0 # simulation domain size in y direction\n", " Lz = 10 * t # simulation domain size in z direction\n", " sim_size = (Lx, Ly, Lz)\n", "\n", " # add a mode source as excitation\n", " mode_spec = td.ModeSpec(num_modes=1, target_neff=3.5)\n", " mode_source = td.ModeSource(\n", " center=(l_in / 2, 0, t / 2),\n", " size=(0, 4 * w_start, 6 * t),\n", " source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n", " direction=\"+\",\n", " mode_spec=mode_spec,\n", " mode_index=0,\n", " )\n", "\n", " # add a mode monitor to measure transmission at the output waveguide\n", " mode_monitor_11 = td.ModeMonitor(\n", " center=(l_in / 3, 0, t / 2),\n", " size=(0, 4 * w_start, 6 * t),\n", " freqs=freqs,\n", " mode_spec=mode_spec,\n", " name=\"mode_11\",\n", " )\n", "\n", " mode_monitor_12 = td.ModeMonitor(\n", " center=(l_in + l_junction + l_bend + l_out / 2, w_end / 2 - w_start / 2 + h_bend, t / 2),\n", " size=(0, 4 * w_start, 6 * t),\n", " freqs=freqs,\n", " mode_spec=mode_spec,\n", " name=\"mode_12\",\n", " )\n", "\n", " # add a field monitor to visualize field distribution at z=t/2\n", " field_monitor = td.FieldMonitor(\n", " center=(0, 0, t / 2), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n", " )\n", "\n", " run_time = 5e-13 # simulation run time\n", "\n", " # construct simulation\n", " sim = td.Simulation(\n", " center=(Lx / 2, 0, 0),\n", " size=sim_size,\n", " grid_spec=td.GridSpec.auto(min_steps_per_wvl=20, wavelength=lda0),\n", " structures=model_structure,\n", " sources=[mode_source],\n", " monitors=[mode_monitor_11, mode_monitor_12, field_monitor],\n", " run_time=run_time,\n", " boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n", " medium=sio2,\n", " )\n", "\n", " return sim\n", "\n", "\n", "def fn_post(sim_data: td.SimulationData) -> float:\n", " \"\"\"Calculate the loss function from the power at the mode monitors in the SimulationData.\"\"\"\n", " # Calculate the power reflected back to source and transmitted to one branch\n", " power_reflected = np.squeeze(\n", " np.abs(sim_data[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2\n", " )\n", " power_transmitted = np.squeeze(\n", " np.abs(sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n", " )\n", "\n", " # Loss function proposed by Gao et al. which takes advantage of branch symmetry\n", " loss_fn = 1 / 3 * n_wav * np.sum(power_reflected**2 + 2 * (power_transmitted - 0.5) ** 2)\n", " output = -float(loss_fn.values) # Negative value as this is a minimizing loss function\n", "\n", " return output" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can quickly check that ``fn_pre`` is working correctly by passing a set of potential ``test_params`` and plotting the result. Note that the gap between the `Polyslab` junction and the branches is a plotting artifact and doesn't exist in the `Simulation`." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "test_params = {\n", " \"w1\": 0.5,\n", " \"w2\": 0.5,\n", " \"w3\": 0.6,\n", " \"w4\": 0.7,\n", " \"w5\": 0.9,\n", " \"w6\": 1.26,\n", " \"w7\": 1.4,\n", " \"w8\": 1.4,\n", " \"w9\": 1.4,\n", " \"w10\": 1.4,\n", " \"w11\": 1.31,\n", " \"w12\": 0.5,\n", " \"w13\": 0.5,\n", "}\n", "sim = fn_pre(**test_params)\n", "sim.plot(z=0)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we setup the Bayesian optimization method. This is done with the ``MethodBayOpt`` object. We don't have the ``lcb`` (lower confidence bound) acquisition function used in the paper available to us, but by making our loss function negative and using the ``ucb`` (upper confidence bound) acquisition we achieve the same optimizer design. The `initial_iter` and `n_iter` options control the number of initial random samples and subsequent optimization iterations respectively. We can also set the random `seed` to ensure reproducible results (optional).\n", "\n", "We also create a list of ``ParameterFloat`` objects corresponding to the 13 segment widths. The `span` option defines the bounds of each parameter. \n", "\n", "The ``method`` and ``parameters`` are then passed to a ``DesignSpace`` object which contains all we need to run the Bayesian optimization." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "method = tdd.MethodBayOpt(\n", " initial_iter=30,\n", " n_iter=70,\n", " acq_func=\"ucb\",\n", " kappa=0.3,\n", " seed=1,\n", ")\n", "\n", "parameters = [tdd.ParameterFloat(name=f\"w_{i}\", span=(0.5, 1.6)) for i in range(num_d)]\n", "\n", "design_space = tdd.DesignSpace(\n", " method=method, parameters=parameters, task_name=\"bay_opt_notebook\", path_dir=\"./data\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is then very easy to the launch this optimization with ``design_space.run()``. This launches an initial random batch of 30 simulations, as specified by `initial_iter`, followed by sequential computation of 70 simulations, as specified by `n_iter`. In the latter phase, the Bayesian optimizer chooses potential candidates based on simultaneously maximizing the objective value and efficiently exploring the design space. The total of 100 simulations takes around 90 minutes to compute. Once complete, the results are returned in a ``pandas`` dataframe for analysis." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
22:01:49 CEST Running 30 Simulations                                            \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:01:49 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m30\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:03:54 CEST Best Fit from Initial Solutions: -181.226                         \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:03:54 CEST\u001b[0m\u001b[2;36m \u001b[0mBest Fit from Initial Solutions: \u001b[1;36m-181.226\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:03 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:03 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:13 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:13 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:21 CEST Latest Best Fit on Iter 2: -150.359                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:21 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m2\u001b[0m: \u001b[1;36m-150.359\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:22 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:22 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:30 CEST Latest Best Fit on Iter 3: -134.853                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:30 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m3\u001b[0m: \u001b[1;36m-134.853\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:31 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:31 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:40 CEST Latest Best Fit on Iter 4: -122.553                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:40 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m4\u001b[0m: \u001b[1;36m-122.553\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:49 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:49 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:04:58 CEST Latest Best Fit on Iter 6: -81.173                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:04:58 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m6\u001b[0m: \u001b[1;36m-81.173\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:05:08 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:05:08 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:05:17 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:05:17 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:05:26 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:05:26 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:05:35 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:05:35 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:05:44 CEST Latest Best Fit on Iter 11: -68.01                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:05:44 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m11\u001b[0m: \u001b[1;36m-68.01\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:05:53 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:05:53 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:06:03 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:06:03 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:06:12 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:06:12 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:06:21 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:06:21 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:06:30 CEST Latest Best Fit on Iter 16: -44.9                                 \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:06:30 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m16\u001b[0m: \u001b[1;36m-44.9\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:06:40 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:06:40 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:06:49 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:06:49 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:06:58 CEST Latest Best Fit on Iter 19: -35.059                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:06:58 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m19\u001b[0m: \u001b[1;36m-35.059\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:07:07 CEST Latest Best Fit on Iter 20: -27.09                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:07:07 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m20\u001b[0m: \u001b[1;36m-27.09\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:07:16 CEST Latest Best Fit on Iter 21: -23.111                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:07:16 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m21\u001b[0m: \u001b[1;36m-23.111\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:07:25 CEST Latest Best Fit on Iter 22: -19.258                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:07:25 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m22\u001b[0m: \u001b[1;36m-19.258\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:07:26 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:07:26 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:07:35 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:07:35 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:07:44 CEST Latest Best Fit on Iter 24: -15.179                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:07:44 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m24\u001b[0m: \u001b[1;36m-15.179\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:07:53 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:07:53 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:08:02 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:08:02 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:08:12 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:08:12 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:08:21 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:08:21 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:08:30 CEST Latest Best Fit on Iter 29: -12.722                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:08:30 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m29\u001b[0m: \u001b[1;36m-12.722\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:08:39 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:08:39 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:08:48 CEST Latest Best Fit on Iter 31: -12.159                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:08:48 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m31\u001b[0m: \u001b[1;36m-12.159\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:08:57 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:08:57 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:06 CEST Latest Best Fit on Iter 33: -11.087                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:06 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m33\u001b[0m: \u001b[1;36m-11.087\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:07 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:07 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:15 CEST Latest Best Fit on Iter 34: -10.551                               \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:15 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m34\u001b[0m: \u001b[1;36m-10.551\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:16 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:16 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:25 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:25 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:34 CEST Latest Best Fit on Iter 36: -7.42                                 \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:34 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m36\u001b[0m: \u001b[1;36m-7.42\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:43 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:43 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:52 CEST Latest Best Fit on Iter 38: -6.507                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:52 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m38\u001b[0m: \u001b[1;36m-6.507\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:09:53 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:09:53 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:02 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:02 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:11 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:11 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:21 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:21 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:30 CEST Latest Best Fit on Iter 42: -6.414                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:30 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m42\u001b[0m: \u001b[1;36m-6.414\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:39 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:39 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:48 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:48 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:57 CEST Latest Best Fit on Iter 45: -5.994                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:57 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m45\u001b[0m: \u001b[1;36m-5.994\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:10:58 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:10:58 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:13:06 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:13:06 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:13:53 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:13:53 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:14:38 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:14:38 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:15:20 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:15:20 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:16:03 CEST Latest Best Fit on Iter 50: -5.513                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:16:03 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m50\u001b[0m: \u001b[1;36m-5.513\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:16:46 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:16:46 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:18:12 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:18:12 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:19:32 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:19:32 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:20:15 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:20:15 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:21:04 CEST Latest Best Fit on Iter 55: -5.463                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:21:04 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m55\u001b[0m: \u001b[1;36m-5.463\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:21:53 CEST Latest Best Fit on Iter 56: -5.229                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:21:53 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m56\u001b[0m: \u001b[1;36m-5.229\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:22:42 CEST Latest Best Fit on Iter 57: -4.891                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:22:42 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m57\u001b[0m: \u001b[1;36m-4.891\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:23:26 CEST Latest Best Fit on Iter 58: -4.182                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:23:26 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m58\u001b[0m: \u001b[1;36m-4.182\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:23:27 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:23:27 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:24:12 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:24:12 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:24:56 CEST Latest Best Fit on Iter 60: -3.962                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:24:56 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m60\u001b[0m: \u001b[1;36m-3.962\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:25:41 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:25:41 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:27:10 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:27:10 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:27:56 CEST Latest Best Fit on Iter 63: -3.735                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:27:56 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m63\u001b[0m: \u001b[1;36m-3.735\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:28:41 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:28:41 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:29:25 CEST Latest Best Fit on Iter 65: -3.544                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:29:25 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m65\u001b[0m: \u001b[1;36m-3.544\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:29:26 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:29:26 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:30:17 CEST Latest Best Fit on Iter 66: -3.42                                 \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:30:17 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m66\u001b[0m: \u001b[1;36m-3.42\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:31:01 CEST Latest Best Fit on Iter 67: -3.288                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:31:01 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m67\u001b[0m: \u001b[1;36m-3.288\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:31:02 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:31:02 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:31:53 CEST Running 1 Simulations                                             \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:31:53 CEST\u001b[0m\u001b[2;36m \u001b[0mRunning \u001b[1;36m1\u001b[0m Simulations \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:33:28 CEST Latest Best Fit on Iter 69: -3.256                                \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:33:28 CEST\u001b[0m\u001b[2;36m \u001b[0mLatest Best Fit on Iter \u001b[1;36m69\u001b[0m: \u001b[1;36m-3.256\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Best Result: -3.255581414447776                                   \n",
       "              Best Parameters: w_0: 0.5188415764684946 w_1: 0.5 w_10: 0.5 w_11: \n",
       "              0.5 w_12: 1.1177014993031638 w_2: 1.1415176565004286 w_3: 1.6 w_4:\n",
       "              1.2733923550648545 w_5: 1.3868594525894349 w_6: 1.419242614773869 \n",
       "              w_7: 1.2066095530458796 w_8: 1.6 w_9: 1.1296647155637014          \n",
       "                                                                                \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mBest Result: \u001b[1;36m-3.255581414447776\u001b[0m \n", "\u001b[2;36m \u001b[0mBest Parameters: w_0: \u001b[1;36m0.5188415764684946\u001b[0m w_1: \u001b[1;36m0.5\u001b[0m w_10: \u001b[1;36m0.5\u001b[0m w_11: \n", "\u001b[2;36m \u001b[0m\u001b[1;36m0.5\u001b[0m w_12: \u001b[1;36m1.1177014993031638\u001b[0m w_2: \u001b[1;36m1.1415176565004286\u001b[0m w_3: \u001b[1;36m1.6\u001b[0m w_4:\n", "\u001b[2;36m \u001b[0m\u001b[1;36m1.2733923550648545\u001b[0m w_5: \u001b[1;36m1.3868594525894349\u001b[0m w_6: \u001b[1;36m1.419242614773869\u001b[0m \n", "\u001b[2;36m \u001b[0mw_7: \u001b[1;36m1.2066095530458796\u001b[0m w_8: \u001b[1;36m1.6\u001b[0m w_9: \u001b[1;36m1.1296647155637014\u001b[0m \n", "\u001b[2;36m \u001b[0m \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "results = design_space.run(fn_pre, fn_post, verbose=True)\n", "df = results.to_dataframe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The best result can be extracted directly from the optimizer object. Plotting this, we see what the optimizer has returned as the optimized structure for this design of Y-junction." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best fitness: -3.255581414447776\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "best_params = results.optimizer.max[\"params\"]\n", "print(f\"Best fitness: {results.optimizer.max['target']}\")\n", "sim = fn_pre(**best_params)\n", "sim.plot(z=0)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can then create a plot to evaluate if the Bayesian optimization has converged on a fitness value. The first 30 iterations are from the random initialization, so the fitness values are expected to be more varied. The fitness has converged before the end of the remaining 70 iterations; an early stop criteria could have been included to finish the optimization sooner." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df[\"output\"].plot(xlabel=\"Simulation Number\", ylabel=\"Fitness\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean power at the reflected and transmitted monitors can be calculated and compared to the paper from ``Gao et al.``" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean Reflected Power: 0.001 (Paper: 0.004)\n", "Mean Transmitted Power: 0.478 (Paper: 0.460)\n" ] } ], "source": [ "idx_best_result = df[\"output\"].idxmax()\n", "best_sim_filename = results.task_paths[idx_best_result]\n", "best_sim = td.SimulationData.from_file(best_sim_filename)\n", "\n", "power_reflected = np.array(\n", " np.squeeze(np.abs(best_sim[\"mode_11\"].amps.sel(direction=\"-\", mode_index=0)) ** 2)\n", ").mean()\n", "power_transmitted = np.array(\n", " np.squeeze(np.abs(best_sim[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2)\n", ").mean()\n", "\n", "print(f\"Mean Reflected Power: {round(power_reflected, 3)} (Paper: 0.004)\")\n", "print(f\"Mean Transmitted Power: {round(power_transmitted, 3)} (Paper: 0.460)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Final Optimized Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can simulate the optimal solution with an additional `FieldMonitor` to visualise the field distribution throughout the splitter." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
              Created task 'BO_notebook_final_sim' with task_id                 \n",
       "              'fdve-1c9aafc2-a589-4866-aa95-880853947d87' and task_type 'FDTD'. \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'BO_notebook_final_sim'\u001b[0m with task_id \n", "\u001b[2;36m \u001b[0m\u001b[32m'fdve-1c9aafc2-a589-4866-aa95-880853947d87'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
22:33:29 CEST View task using web UI at                                         \n",
       "              'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a5\n",
       "              89-4866-aa95-880853947d87'.                                       \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m22:33:29 CEST\u001b[0m\u001b[2;36m \u001b[0mView task using web UI at \n", "\u001b[2;36m \u001b[0m\u001b]8;id=697012;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=740802;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=697012;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=665383;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=697012;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[32m-1c9aafc2-a5\u001b[0m\u001b]8;;\u001b\\\n", "\u001b[2;36m \u001b[0m\u001b]8;id=697012;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[32m89-4866-aa95-880853947d87'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
              Task folder: 'default'.                                           \n",
       "
\n" ], "text/plain": [ "\u001b[2;36m \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=332099;https://tidy3d.simulation.cloud/folders/folder-7a0ee478-ee62-43e0-9a9e-26a06b299b0a\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\. \n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a61bac012bcc48b6968b581b77ac3030", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:33:30 CEST Maximum FlexCredit cost: 0.111. Minimum cost depends on task      \n",
       "              execution details. Use 'web.real_cost(task_id)' to get the billed \n",
       "              FlexCredit cost after a simulation run.                           \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:33:30 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.111\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:33:31 CEST status = queued                                                   \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:33:31 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = queued                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
              To cancel the simulation, use 'web.abort(task_id)' or             \n",
       "              'web.delete(task_id)' or abort/delete the task in the web UI.     \n",
       "              Terminating the Python script will not stop the job running on the\n",
       "              cloud.                                                            \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTo cancel the simulation, use \u001b[32m'web.abort\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or             \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'web.delete\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m or abort/delete the task in the web UI.     \n",
       "\u001b[2;36m              \u001b[0mTerminating the Python script will not stop the job running on the\n",
       "\u001b[2;36m              \u001b[0mcloud.                                                            \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8fd91df5a7a84ee0a79146e53c4cecfb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:34:45 CEST status = preprocess                                               \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:34:45 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = preprocess                                               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:34:49 CEST starting up solver                                                \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:34:49 CEST\u001b[0m\u001b[2;36m \u001b[0mstarting up solver                                                \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
              running solver                                                    \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mrunning solver                                                    \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6ff729fac004b54b5ab0caf5afafd06",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:35:11 CEST early shutoff detected at 76%, exiting.                           \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:35:11 CEST\u001b[0m\u001b[2;36m \u001b[0mearly shutoff detected at \u001b[1;36m76\u001b[0m%, exiting.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:35:12 CEST status = postprocess                                              \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:35:12 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = postprocess                                              \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1b039db030d94c06a822409b003d940a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:35:14 CEST status = success                                                  \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:35:14 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:35:16 CEST View simulation result at                                         \n",
       "              'https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a5\n",
       "              89-4866-aa95-880853947d87'.                                       \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:35:16 CEST\u001b[0m\u001b[2;36m \u001b[0mView simulation result at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=859495;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[4;34m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=981851;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[4;34mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=859495;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[4;34m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=831833;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[4;34mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=859495;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[4;34m-1c9aafc2-a5\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=859495;https://tidy3d.simulation.cloud/workbench?taskId=fdve-1c9aafc2-a589-4866-aa95-880853947d87\u001b\\\u001b[4;34m89-4866-aa95-880853947d87'\u001b[0m\u001b]8;;\u001b\\\u001b[4;34m.\u001b[0m                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b85e23a1d04a4b7ab3d930f740c9a91c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
22:35:20 CEST loading simulation from simulation_data.hdf5                      \n",
       "
\n"
      ],
      "text/plain": [
       "\u001b[2;36m22:35:20 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_sim = fn_pre(**best_params)\n",
    "\n",
    "# Define a field monitor to help visualize the field distribution\n",
    "field_monitor = td.FieldMonitor(\n",
    "    center=(0, 0, 0), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"field\"\n",
    ")\n",
    "\n",
    "mode_12 = final_sim.monitors[1]\n",
    "\n",
    "final_sim = final_sim.copy(update={\"monitors\": (field_monitor, mode_12)})\n",
    "\n",
    "final_sim_data = web.run(final_sim, task_name=\"BO_notebook_final_sim\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the field shows how it splits evenly between each branch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_sim_data.plot_field(\"field\", \"E\", \"abs^2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can visualise how the power transmitted varies over the frequency range. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "
"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "power_transmitted = np.squeeze(\n",
    "    np.abs(final_sim_data[\"mode_12\"].amps.sel(direction=\"+\", mode_index=0)) ** 2\n",
    ")\n",
    "plt.plot(freqs, power_transmitted)\n",
    "plt.title(\"Power transmitted across frequency range\")\n",
    "plt.xlabel(\"Frequency / Hz\")\n",
    "plt.ylabel(\"Power\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Through comparison of the junction geometry and the transmitted and reflected power, we can show that these results closely follow the results published by ``Gao et al.`` for the design of a Y-junction. This notebook demonstrates how to carry out Bayesian optimization with the Design plugin, and can be readily adapted to other use cases.\n",
    "\n",
    "Learn more about the Design plugin:\n",
    "\n",
    "1. [Intro to Design Plugin](https://www.flexcompute.com/tidy3d/examples/notebooks/Design/)\n",
    "\n",
    "2. [Particle Swarm Optimizer PBS](https://www.flexcompute.com/tidy3d/examples/notebooks/ParticleSwarmOptimizedPBS/)\n",
    "\n",
    "3. [Particle Swarm Optimizer Bullseye Cavity](https://www.flexcompute.com/tidy3d/examples/notebooks/BullseyeCavityPSO/)\n",
    "\n",
    "4. [Genetic Algorithm Reflector](https://www.flexcompute.com/tidy3d/examples/notebooks/GeneticAlgorithmReflector/)\n",
    "\n",
    "5. [All-Dielectric Structural Colors](https://www.flexcompute.com/tidy3d/examples/notebooks/AllDielectricStructuralColor/)\n",
    "\n",
    "6. [Parameter Scan](https://www.flexcompute.com/tidy3d/examples/notebooks/ParameterScan/)"
   ]
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components"
  ],
  "description": "Bayesian optimization is a popular technique to search a design space. In this notebook Bayesian optimization is used to optimize a Y-junction to transmit power equally through each branch.",
  "feature_image": "./img/bayesian_opt_y_junction.png",
  "features": [
   "Design plugin",
   "Global optimization"
  ],
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "keywords": "Bayesian optimization, Y Junction, FDTD, Tidy3d",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  },
  "title": "How to optimize a Y-junction using Bayesian optimization with Tidy3D FDTD simulations"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}