{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Autograd, automatic differentiation, and adjoint optimization: basics\n",
        "\n",
        "## Introduction\n",
        "\n",
        "In this notebook, we will introduce the automatic differentiation\n",
        "feature of Tidy3D. This feature allows users to take derivatives of\n",
        "arbitrary functions involving Tidy3D simulations through the use of the\n",
        "“adjoint method”. The advantage of the adjoint method is that the\n",
        "gradients can be computed using only **two** FDTD simulations,\n",
        "independent of the number of parameters. This makes it possible to do\n",
        "gradient-based optimization or sensitivity analysis of devices with\n",
        "enormous numbers of parameters with minimal computational overhead. For\n",
        "more information on the technical details of the adjoint method and what\n",
        "it can be used for, we recommend these references (with links to their\n",
        "pre-print versions):\n",
        "\n",
        "- [Gradient-based “inverse design” optimization in\n",
        "  photonics](https://arxiv.org/pdf/1801.06715.pdf).\n",
        "\n",
        "- [Adjoint method for\n",
        "  electromagnetics](https://arxiv.org/abs/1908.10507)\n",
        "\n",
        "If you want to skip to some case studies of this technique being used\n",
        "for some applications, see the\n",
        "\n",
        "- [Gradient Checking\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd2GradientChecking/).\n",
        "\n",
        "- [Inverse Design\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd3InverseDesign/).\n",
        "\n",
        "- [Multi-Objective Gradient\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd4MultiObjective/).\n",
        "\n",
        "- [Boundary Gradients\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd5BoundaryGradients/).\n",
        "\n",
        "- [Grating Coupler\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd6GratingCoupler/).\n",
        "\n",
        "### Function differentiation\n",
        "\n",
        "The adjoint package enables users to take derivatives of functions that\n",
        "involve a Tidy3D simulation. For a bit of context, let’s first talk\n",
        "about what we mean when we talk about differentiating functions in our\n",
        "programs. Say in our program we have programmatically defined a function\n",
        "of one variable $f(x)$. For example:\n",
        "\n",
        "``` py\n",
        "def f(x):\n",
        "    return x**2\n",
        "```\n",
        "\n",
        "Now, we wish to evaluate $\\frac{df(x)}{dx}$.\n",
        "\n",
        "If we know $\\frac{df}{dx}$ analytically, this is just a matter of\n",
        "writing a new function to compute this derivative, for example:\n",
        "\n",
        "``` py\n",
        "def df(x):\n",
        "    return 2 * x\n",
        "```\n",
        "\n",
        "However, many of the more interesting and complex functions tend to\n",
        "compose several sub-functions together, for example\n",
        "$$f(x) = h(y) = h(g(x))$$ might be written in a program as\n",
        "\n",
        "``` py\n",
        "def f(x):\n",
        "    y = x**2\n",
        "    return 5 * y - y**3\n",
        "```\n",
        "\n",
        "As one can imagine, defining the derivative by hand can quickly become\n",
        "too daunting of a task as the complexity grows.\n",
        "\n",
        "However, we can simplify things greatly with knowledge of the\n",
        "derivatives of the simpler functions that make up $f$. Following the\n",
        "chain rule, we can write the derivative of $f$ above as\n",
        "$$\\frac{df}{dx}=\\frac{dh}{dy}\\frac{dg}{dx}(x)$$\n",
        "\n",
        "Thus, if we know the derivatives of the composite functions $h$ and $g$,\n",
        "we can construct the derivative of $f$ by multiplying all of the\n",
        "derivatives of the composite functions. This idea is straightforwardly\n",
        "generalized to functions of several inputs and outputs, and even\n",
        "functions that can be written more generally as a more complex\n",
        "“computational graph” rather than a sequence of operations.\n",
        "\n",
        "### Automatic differentiation\n",
        "\n",
        "The idea of a technique called “automatic differentiation” is to provide\n",
        "a way to compute these derivatives of composite functions in programming\n",
        "languages both efficiently and without the user needing to define\n",
        "anything by hand.\n",
        "\n",
        "Automatic differentiation works by defining “derivative rules” for each\n",
        "fundamental operation that the user might incorporate in his or her\n",
        "function. For example, derivative rules for `h` and `g`, in the example\n",
        "above may be used to help define the derivative for `f`. When the\n",
        "function is evaluated, all of the derivative information corresponding\n",
        "to each operation in the function are stitched together using the chain\n",
        "rule to construct a derivative for the entire function. Thus, functions\n",
        "of arbitrary complexity can be differentiated without deriving anything\n",
        "beyond just the derivative rules for the most basic operations contained\n",
        "within.\n",
        "\n",
        "This capability is provided by many programming packages, but we chose\n",
        "to utilize one from the `autograd` package as it provides the\n",
        "flexibility and extendibility we needed for integrating this\n",
        "functionality into Tidy3D.\n",
        "\n",
        "Using `autograd`, we may write a function $f$ using most of the\n",
        "fundamental operations in python and `numpy`. In `autograd` both the\n",
        "operations and their derivatives are tracked when the function is\n",
        "called. Thus, `autograd` gives the option to apply `autograd.grad` to\n",
        "this function, which uses all of the derivative information and the\n",
        "chain rule to construct a new function that gives the derivative of the\n",
        "function with respect to its input arguments.\n",
        "\n",
        "In `tidy3d`, we can track functions **that involve Tidy3D simulations**\n",
        "in their computational graph. In essence, we provide the “derivative” of\n",
        "the `tidy3d.web.run()` function, using the adjoint method, to tell\n",
        "`autograd` how to differentiate functions that might involve both the\n",
        "setting up and postprocessing of a tidy3d simulation and its data. The\n",
        "end result is a framework where users can set up modeling and\n",
        "optimizations and utilize `autograd` automatic differentiation for\n",
        "optimization and sensitivity analysis efficiently and without needing to\n",
        "derive a single derivative rule.\n",
        "\n",
        "In this notebook, we will give an overview of how `autograd` works for\n",
        "beginners and provide a simple example of the plugin. More complex case\n",
        "studies and examples will be provided in other notebooks, linked here:\n",
        "\n",
        "- [Gradient Checking\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd2GradientChecking/).\n",
        "\n",
        "- [Inverse Design\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd3InverseDesign/).\n",
        "\n",
        "- [Multi-Objective Gradient\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd4MultiObjective/).\n",
        "\n",
        "- [Boundary Gradients\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd5BoundaryGradients/).\n",
        "\n",
        "- [Grating Coupler\n",
        "  Notebook](https://www.flexcompute.com/tidy3d/examples/notebooks/Autograd6GratingCoupler/).\n",
        "\n",
        "## Automatic Differentiation using `autograd`\n",
        "\n",
        "Before jumping into any Tidy3D simulations, we will give a bit of a\n",
        "primer on using `autograd` for automatic differentiation.\n",
        "\n",
        "First, we will import `autograd` and its `numpy` wrapper, which provides\n",
        "most of the same functionality, but allows derivative tracking.\n",
        "\n",
        "Tip 1: if you run into an obscure error using `autograd`, the first\n",
        "thing to check is whether you’re using the `autograd.numpy` wrapper\n",
        "instead of regular `numpy` in your function, as otherwise you will get\n",
        "errors from `autograd` that are not super clear."
      ],
      "id": "ff7d9b11-3a00-4f31-98ea-ac046d52648b"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import autograd as ag\n",
        "import autograd.numpy as anp\n",
        "import matplotlib.pylab as plt"
      ],
      "id": "b8270287"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Say we have a function $f$ that performs several operations on a single\n",
        "variable.\n",
        "\n",
        "We can define this function `f` in python and also derive its derivative\n",
        "for this simple case, which we write as a function `df`."
      ],
      "id": "7bcf9235-aec4-4423-8c73-73913810f110"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "def f(x):\n",
        "    return 5 * anp.sin(x) - x**2 + x\n",
        "\n",
        "\n",
        "def df(x):\n",
        "    return 5 * anp.cos(x) - 2 * x + 1"
      ],
      "id": "2fb5ebe3"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let’s evaluate these functions at several points and plot them."
      ],
      "id": "1d2178ac-7dae-4722-b118-e547e85df3b7"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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Ili1bVuBOYVlZWfz+u3ME/IgRI0rmk5Ji5bZh0KlTp+jbty+JiYk0bNiQX375Jd/K6Y88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\nA4ogAAAAoIiVqiANmptRCF06m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          }
        }
      ],
      "source": [
        "xs = anp.linspace(-1, 3, 1001)\n",
        "plt.plot(xs, f(xs), label=\"f(x)\")\n",
        "plt.plot(xs, df(xs), label=\"df/dx(x)\")\n",
        "plt.xlabel(\"x\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "936645dc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now let’s use autograd to compute the derivative for us and see how it\n",
        "compares to our analytical derivative.\n",
        "\n",
        "We first call `autograd.grad(f)`, which returns a new function that can\n",
        "be evaluated at `x` to give the derivative `df/dx(x)`."
      ],
      "id": "21b2e9b0-d360-4eef-a626-46ed916c0977"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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HpUV/lzpfKvW5Xmp/geTj48KkAAAAAMzglWVQhw4dtGrVKklS69ata7zv+GuZmZkuz+V1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          }
        }
      ],
      "source": [
        "# get the gradient as a function of x\n",
        "df_ag = ag.grad(f)\n",
        "\n",
        "# get a set of points to feed the derivative function one by one (for now)\n",
        "xs_ag = anp.linspace(-1, 3, 21)\n",
        "df_ag_eval = [df_ag(x) for x in xs_ag]\n",
        "\n",
        "plt.plot(xs, f(xs), label=\"f(x)\")\n",
        "plt.plot(xs, df(xs), label=\"df/dx(x) [analytical]\")\n",
        "plt.plot(xs_ag, df_ag_eval, \"k.\", label=\"df/dx(x) [using autograd]\")\n",
        "plt.xlabel(\"x\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "fc0933cb"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Note: `autograd` provides several other useful gradient wrappers, which\n",
        "can be used in different contexts.\n",
        "\n",
        "For example `autograd.value_and_grad` returns both the function return\n",
        "value and the gradient value, which is useful to avoid repetitive\n",
        "computation if you need the value as the `autograd.grad` call must\n",
        "evaluate `f`."
      ],
      "id": "8dca577d-910c-462c-be3e-780c224fd135"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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6vGvjgRxtOXRMuYUlF/Rx/by91K5BHbVvGHLyLSrET7bCkj/fipVb4Py17N+X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\n/f2r9frvPOMjOV4W6V83xOqvQ9rIYrGYlA6eLC4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Pr5K+vFOy26s5IYCaICzQV4njeqnO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          }
        }
      ],
      "source": [
        "f_and_df = ag.value_and_grad(f)\n",
        "vals_and_grads = [f_and_df(x) for x in xs_ag]\n",
        "fs, dfs = list(zip(*vals_and_grads))\n",
        "\n",
        "plt.plot(xs_ag, fs, label=\"f(x)\")\n",
        "plt.plot(xs, df(xs), label=\"df/dx(x) [analytical]\")\n",
        "plt.plot(xs_ag, df_ag_eval, \"k.\", label=\"df/dx(x) [using autograd]\")\n",
        "plt.xlabel(\"x\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "id": "2280acd8"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Before we continue, there are a few things to watch out for when using\n",
        "autograd for gradient calculation:\n",
        "\n",
        "1.  `autograd.grad` doesn’t automatically convert input arguments from\n",
        "    `int` to `float`, so avoid passing `int` types to your functions."
      ],
      "id": "67b61c34-d388-41d9-b933-14f59d68546b"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.7015115293406988\n",
            "TypeError(\"Can't differentiate w.r.t. type <class 'int'>\")"
          ]
        }
      ],
      "source": [
        "# ok\n",
        "print(df_ag(1.0))\n",
        "\n",
        "# errors\n",
        "try:\n",
        "    df_ag(1)\n",
        "except TypeError as e:\n",
        "    print(repr(e))"
      ],
      "id": "91afcaf9"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "1.  When differentiating with respect to several arguments, you need to\n",
        "    tell `autograd.grad` which arguments you want to take the derivative\n",
        "    with respect to as a tuple in indices. Otherwise it will take the\n",
        "    derivative with respect to only the first argument."
      ],
      "id": "6de82b1f-7ea3-4210-8940-1f35d4cab334"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dgdx=1.0\n",
            "dgdx=1.0, dgdy=1.0, dgdz=2.0"
          ]
        }
      ],
      "source": [
        "def g(x, y, z):\n",
        "    return x * y + z**2\n",
        "\n",
        "\n",
        "# only gives dg/dx\n",
        "dg = ag.grad(g)\n",
        "dgdx = dg(1.0, 1.0, 1.0)\n",
        "print(f\"dgdx={dgdx}\")\n",
        "\n",
        "# gives derivative w.r.t. all three args\n",
        "dg_all = ag.grad(g, argnum=(0, 1, 2))\n",
        "dgdx, dgdy, dgdz = dg_all(1.0, 1.0, 1.0)\n",
        "print(f\"dgdx={dgdx}, dgdy={dgdy}, dgdz={dgdz}\")"
      ],
      "id": "2d8dbe77"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Incorporating Automatic Differentiation in `Tidy3D`\n",
        "\n",
        "With that basic introduction to automatic differentiation using\n",
        "`autograd`, we can now show how `Tidy3D` lets us do the same thing but\n",
        "where our functions can now involve setting up, running, and\n",
        "postprocessing a `tidy3d.Simulation`."
      ],
      "id": "174da786-1d25-44c1-918d-b289517ebf92"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "import tidy3d as td\n",
        "\n",
        "td.config.logging.level = \"INFO\""
      ],
      "id": "1da57249"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Simulation example\n",
        "\n",
        "In our example, we will set up a function that involves a simulation of\n",
        "transmission through a waveguide in the presence of a scatterer.\n",
        "\n",
        "This scatterer geometry and material properties will depend on the\n",
        "function input arguments.\n",
        "\n",
        "The output of the function will simply be the power transmitted into the\n",
        "0th order mode.\n",
        "\n",
        "We will then take the gradient of the output of this function (power)\n",
        "with respect to the scatterer geometric and medium properties using\n",
        "`autograd`.\n",
        "\n",
        "To start, it can often be helpful to break our function up into a few\n",
        "parts for debugging.\n",
        "\n",
        "Therefore, we will introduce one function to make the `td.Simulation`\n",
        "given the input arguments and one function to postprocess the result."
      ],
      "id": "95422c99-e378-4ed6-a392-a48fdfc4d0e4"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "def make_simulation(center: tuple, size: tuple, eps: float) -> td.Simulation:\n",
        "    \"\"\"Makes a simulation with a variable scatter width, height, and relative permittivity.\"\"\"\n",
        "\n",
        "    wavelength = 1.0\n",
        "    freq0 = td.C_0 / wavelength\n",
        "    dl = 0.02\n",
        "\n",
        "    # a \"static\" structure\n",
        "    waveguide = td.Structure(\n",
        "        geometry=td.Box(size=(td.inf, 0.3, 0.2)), medium=td.Medium(permittivity=2.0)\n",
        "    )\n",
        "\n",
        "    # our \"forward\" source\n",
        "    mode_src = td.ModeSource(\n",
        "        size=(0, 1.5, 1.5),\n",
        "        center=(-0.9, 0, 0),\n",
        "        mode_index=0,\n",
        "        source_time=td.GaussianPulse(freq0=freq0, fwidth=freq0 / 10),\n",
        "        direction=\"+\",\n",
        "    )\n",
        "\n",
        "    # a monitor to store data that our overall function will depend on\n",
        "    mode_mnt = td.ModeMonitor(\n",
        "        size=(0, 1.5, 1.5),\n",
        "        center=(+0.9, 0, 0),\n",
        "        mode_spec=mode_src.mode_spec,\n",
        "        freqs=[freq0],\n",
        "        name=\"mode\",\n",
        "    )\n",
        "\n",
        "    # the structure that depends on the input parameters, which we will differentiate our function w.r.t\n",
        "    scatterer = td.Structure(\n",
        "        geometry=td.Box(\n",
        "            center=center,\n",
        "            size=size,\n",
        "        ),\n",
        "        medium=td.Medium(permittivity=eps),\n",
        "    )\n",
        "\n",
        "    return td.Simulation(\n",
        "        size=(2, 2, 2),\n",
        "        run_time=1e-12,\n",
        "        structures=[waveguide, scatterer],\n",
        "        sources=[mode_src],\n",
        "        monitors=[mode_mnt],\n",
        "        boundary_spec=td.BoundarySpec.all_sides(td.PML()),\n",
        "        grid_spec=td.GridSpec.uniform(dl=dl),\n",
        "    )"
      ],
      "id": "0f56532b"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Let’s try setting up the simulation and plotting it for starters."
      ],
      "id": "ac9c9055-bfa7-41a7-b398-dc10d3b08413"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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8oEsvvdRZ3n777TVu3DgNGTJE7du315o1a/Tpp5/qjjvu0M8//6xgMKgTTjhB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\n30OxLCvq4gCx78u4sY10ubGvE044wXm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mevsvKytTcXHzlx1MOf+Z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\n/Onid37T67/aWMQHV+Tk5KTliQy/B7DNr4Hs9wnc5teJnPzkJ3/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          }
        }
      ],
      "source": [
        "# starting set of input parameters\n",
        "center0 = (0.0, 0.0, 0.0)\n",
        "size0 = (0.5, 1.0, 1.0)\n",
        "eps0 = 3.0\n",
        "\n",
        "sim = make_simulation(center=center0, size=size0, eps=eps0)\n",
        "_, axes = plt.subplots(1, 3, figsize=(16, 5))\n",
        "\n",
        "for ax, dim in zip(axes, \"xyz\"):\n",
        "    sim.plot(**{dim: 0}, ax=ax)\n",
        "plt.show()"
      ],
      "id": "52122806"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Post-processing the output data\n",
        "\n",
        "After the simulation is run, it returns a `td.SimulationData` containing\n",
        "the data we want to post-process.\n",
        "\n",
        "Let’s write a function that will process this `td.SimulationData` and\n",
        "return the power in the mode amplitude of our output mode monitor."
      ],
      "id": "7d229032-bf1b-4194-bd98-88ccd7a8f329"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "def compute_power(sim_data: td.SimulationData) -> float:\n",
        "    \"\"\"Post process the result of the Simulation run to return the power in the mode at index=0.\"\"\"\n",
        "\n",
        "    freq0 = sim_data.simulation.monitors[0].freqs[0]\n",
        "    mode_data = sim_data[\"mode\"]\n",
        "    mode_amps = mode_data.amps\n",
        "    amp = mode_amps.sel(direction=\"+\", f=freq0, mode_index=0).values\n",
        "    return anp.sum(abs(amp) ** 2)"
      ],
      "id": "47cec04e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Defining the tidy3d simulation function for differentiation\n",
        "\n",
        "Next, we can import the `tidy3d..web.run` function and put all the\n",
        "pieces together into a single function to compute the 0th order\n",
        "transmitted power as a function of `center`, `size`, and `eps` (relative\n",
        "permittivity) of the scatterer."
      ],
      "id": "adfa6c59-537d-4916-9953-fd3cbb2e12a1"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [],
      "source": [
        "import tidy3d.web as web"
      ],
      "id": "1b1ed3ba"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "def power(center: tuple, size: tuple, eps: float) -> float:\n",
        "    \"\"\"Compute power transmitted into 0th order mode given a set of scatterer parameters.\"\"\"\n",
        "    sim = make_simulation(center=center, size=size, eps=eps)\n",
        "    sim_data = web.run(sim, task_name=\"autograd 1\")\n",
        "    return compute_power(sim_data)"
      ],
      "id": "e81bdc57"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:26 UTC </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'autograd 1'</span> with resource_id                         \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-a8dbd34c-5a1d-44db-ba8c-5b202938d44b'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a8dbd34c-5a1d-44db-ba8c-5b202938d44b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a8dbd34c-5a1</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a8dbd34c-5a1d-44db-ba8c-5b202938d44b\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">d-44db-ba8c-5b202938d44b'</span></a>.                                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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        {
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:27 UTC </span>Estimated FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span>. This assumes the FDTD solver runs\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>for the full simulation time; if early shutoff is reached, the     \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed cost can be lower. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the  \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>billed FlexCredit cost after a simulation run.                     \n",
              "</pre>"
            ]
          }
        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:28 UTC </span>status = success                                                   \n",
              "</pre>"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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        {
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:29 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
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          "metadata": {},
          "data": {
            "text/plain": [
              "np.float64(0.5464556565105481)"
            ]
          }
        }
      ],
      "source": [
        "power(center0, size0, eps0)"
      ],
      "id": "17aef21e"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Running and differentiating the simulation using `autograd`\n",
        "\n",
        "Finally, using the `autograd` tools described earlier, we can\n",
        "differentiate this `power` function.\n",
        "\n",
        "For demonstration, let’s use `autograd.value_and_grad` to both compute\n",
        "the power **and** the gradient w.r.t. each of the 3 input parameters."
      ],
      "id": "2b9e4a2f-fcd8-47b1-a9f1-309b138f0946"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "d_power = ag.value_and_grad(power, argnum=(0, 1, 2))"
      ],
      "id": "09acfde2"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will run this function and assign variables to the power values and\n",
        "the gradients returned.\n",
        "\n",
        "Note that running this will set off **two** separate tasks, one after\n",
        "another, called, `\"adjoint_power_fwd\"` and `\"adjoint_power_adj\"`.\n",
        "\n",
        "The first is evaluating our simulation in “forward mode”, computing the\n",
        "power and stashing information needed for gradient computation.\n",
        "\n",
        "The second step runs the “adjoint” simulation, in which the output\n",
        "monitor is converted to a source and the simulation is re-run.\n",
        "\n",
        "The results of both of these simulations runs are combined behind the\n",
        "scene to tell `autograd` how to compute the gradient for us."
      ],
      "id": "e04289c8-0ba7-4cb9-bc30-33b88e516e7b"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: running primitive <span style=\"color: #008000; text-decoration-color: #008000\">'_run_primitive()'</span>                         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: running autograd_fwd simulation with <span style=\"color: #008000; text-decoration-color: #008000\">'_run_tidy3d()'</span>         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'autograd 1'</span> with resource_id                         \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-431626fa-52d2-4e16-b0d6-149f86f61ffa'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>.  \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>View task using web UI at                                          \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-431626fa-52d2-4e16-b0d6-149f86f61ffa\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-431626fa-52d</span></a>\n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-431626fa-52d2-4e16-b0d6-149f86f61ffa\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">2-4e16-b0d6-149f86f61ffa'</span></a>.                                         \n",
              "</pre>"
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        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/9b36e144-ddb6-41f8-8dd8-30b62b26a870\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                            \n",
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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        {
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:32 UTC </span>status = success                                                   \n",
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:33 UTC </span>Loading results from simulation_data.hdf5                          \n",
              "</pre>"
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        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Number of fields to compute gradients for: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>                 \n",
              "</pre>"
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        },
        {
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          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Using server-side gradient computation mode                  \n",
              "</pre>"
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        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Constructing custom VJP function for backwards pass.         \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Running custom vjp <span style=\"font-weight: bold\">(</span>adjoint<span style=\"font-weight: bold\">)</span> pipeline.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Created <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> adjoint sources for monitor <span style=\"color: #008000; text-decoration-color: #008000\">'mode'</span>.                \n",
              "</pre>"
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        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Found <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> spatial ports and <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> unique frequencies.              \n",
              "</pre>"
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        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Grouping adjoint sources by frequency.                       \n",
              "</pre>"
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          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Created <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> adjoint source groups.                             \n",
              "</pre>"
            ]
          }
        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Created <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> adjoint simulations.                               \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Running <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> adjoint simulations                                \n",
              "</pre>"
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        },
        {
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          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Starting server-side batch of adjoint simulations <span style=\"color: #808000; text-decoration-color: #808000\">...</span>        \n",
              "</pre>"
            ]
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        },
        {
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          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: running autograd_bwd batch with <span style=\"color: #008000; text-decoration-color: #008000\">'_run_async_tidy3d_bwd()'</span>    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span> tasks.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:36 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.025</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
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          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after    \n",
              "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>completion.                                                        \n",
              "</pre>"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">02:12:37 UTC </span>Batch complete.                                                    \n",
              "</pre>"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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            "text/html": [
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>"
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        },
        {
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          "data": {
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">             </span>INFO: Completed server-side batch of adjoint simulations.          \n",
              "</pre>"
            ]
          }
        }
      ],
      "source": [
        "power_value, (dp_dcenter, dp_dsize, dp_deps) = d_power(center0, size0, eps0)"
      ],
      "id": "9260c1dc"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "> Note: the gradient evaluation functions returned by `autograd.grad()`\n",
        "> do not accept keyword arguments (ie. `center=(0.,0.,0.)`) and instead\n",
        "> accept positional arguments (without the argument name). You may run\n",
        "> across this when trying to evaluate gradients so it’s a good idea to\n",
        "> keep in mind.\n",
        "\n",
        "We can take a look at our computed power and gradient information."
      ],
      "id": "72fa140a-e832-40b3-a48f-1cff4a94ca60"
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "power = 0.546\n",
            "d_power/d_center = (array(0.00481733), array(1.8492815e-08), array(6.27205594e-07))\n",
            "d_power/d_size = (array(0.00018005), array(0.12404339), array(-0.16391527))\n",
            "d_power/d_eps = -0.1566309009901546"
          ]
        }
      ],
      "source": [
        "print(f\"power = {power_value:.3f}\")\n",
        "print(f\"d_power/d_center = {dp_dcenter}\")\n",
        "print(f\"d_power/d_size = {dp_dsize}\")\n",
        "print(f\"d_power/d_eps = {dp_deps}\")"
      ],
      "id": "19057f97"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "From this, we can infer several things that fit our intuition, for\n",
        "example that: \\* the transmitted power should **decrease** if we\n",
        "increase the permittivity of our scatterer. \\* the transmitted power\n",
        "does not depend strongly on the position of the scatterer along the\n",
        "propagation direction.\n",
        "\n",
        "## Conclusion & Next Steps\n",
        "\n",
        "This gives the most basic introduction to the principles behind autograd\n",
        "and the adjoint method.\n",
        "\n",
        "In subsequent notebooks, we will show how to: \\* Check the gradients\n",
        "returned by this method against brute force computed gradients for\n",
        "accuracy. \\* Perform gradient-based optimization using autograd."
      ],
      "id": "ad46597a-a10c-493f-975f-ef5461bc3d68"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}