{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "expmkveO04pw" }, "source": [ "## Parameter Estimation with SciPy's Differential Evolution Method in PyBOP\n", "\n", "In this notebook, we demonstrate an example of parameter estimation for a single-particle model using the Differential Evolution optimiser. The Differential Evolution (DE) algorithm is a stochastic population-based method that is well-suited for multi-dimensional functions. Unlike gradient-based methods, which require gradient information and can easily become trapped in local minima, DE relies on the concept of evolving a population of candidate solutions, using operations like mutation, crossover, and selection. This approach allows DE to search large areas of the solution space, which makes it highly effective for dealing with complex, nonlinear, and multimodal objective functions. \n", "\n", "### Setting up the Environment\n", "\n", "Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP from its development branch and upgrade some dependencies:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X87NUGPW04py", "outputId": "0d785b07-7cff-4aeb-e60a-4ff5a669afbf" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pip in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (23.3.2)\n", "Requirement already satisfied: ipywidgets in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (8.1.1)\n", "Requirement already satisfied: comm>=0.1.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (0.2.0)\n", "Requirement already satisfied: ipython>=6.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (8.18.1)\n", "Requirement already satisfied: traitlets>=4.3.1 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (5.14.0)\n", "Requirement already satisfied: widgetsnbextension~=4.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (4.0.9)\n", "Requirement already satisfied: jupyterlab-widgets~=3.0.9 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipywidgets) (3.0.9)\n", "Requirement already satisfied: decorator in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (5.1.1)\n", "Requirement already satisfied: jedi>=0.16 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.19.1)\n", "Requirement already satisfied: matplotlib-inline in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.1.6)\n", "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (3.0.41)\n", "Requirement already satisfied: pygments>=2.4.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (2.17.2)\n", "Requirement already satisfied: stack-data in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (0.6.3)\n", "Requirement already satisfied: pexpect>4.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from ipython>=6.1.0->ipywidgets) (4.9.0)\n", "Requirement already satisfied: parso<0.9.0,>=0.8.3 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from jedi>=0.16->ipython>=6.1.0->ipywidgets) (0.8.3)\n", "Requirement already satisfied: ptyprocess>=0.5 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from pexpect>4.3->ipython>=6.1.0->ipywidgets) (0.7.0)\n", "Requirement already satisfied: wcwidth in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.1.0->ipywidgets) (0.2.12)\n", "Requirement already satisfied: executing>=1.2.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.0.1)\n", "Requirement already satisfied: asttokens>=2.1.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (2.4.1)\n", "Requirement already satisfied: pure-eval in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from stack-data->ipython>=6.1.0->ipywidgets) (0.2.2)\n", "Requirement already satisfied: six>=1.12.0 in /Users/bradyplanden/.pyenv/versions/pybop-env/lib/python3.11/site-packages (from asttokens>=2.1.0->stack-data->ipython>=6.1.0->ipywidgets) (1.16.0)\n", "Note: you may need to restart the kernel to use updated packages.\n", "Note: you may need to restart the kernel to use updated packages.\n" ] } ], "source": [ "%pip install --upgrade pip ipywidgets\n", "%pip install pybop -q" ] }, { "cell_type": "markdown", "metadata": { "id": "jAvD5fk104p0" }, "source": [ "### Importing Libraries\n", "\n", "With the environment set up, we can now import PyBOP alongside other libraries we will need:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "id": "SQdt4brD04p1" }, "outputs": [], "source": [ "import pybop\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": { "id": "5XU-dMtU04p2" }, "source": [ "### Generate Synthetic Data\n", "\n", "To demonstrate parameter estimation, we first need some data. We will generate synthetic data using the PyBOP forward model, which requires defining a parameter set and the model itself.\n", "\n", "#### Defining Parameters and Model\n", "\n", "We start by creating an example parameter set and then instantiate the single-particle model (SPM):" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "parameter_set = pybop.ParameterSet.pybamm(\"Chen2020\")\n", "model = pybop.lithium_ion.SPM(parameter_set=parameter_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simulating Forward Model\n", "\n", "We can then simulate the model using the `predict` method, with a default constant current to generate voltage data." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "id": "sBasxv8U04p3" }, "outputs": [], "source": [ "t_eval = np.arange(0, 900, 2)\n", "values = model.predict(t_eval=t_eval)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding Noise to Voltage Data\n", "\n", "To make the parameter estimation more realistic, we add Gaussian noise to the data." ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "sigma = 0.001\n", "corrupt_values = values[\"Voltage [V]\"].data + np.random.normal(0, sigma, len(t_eval))" ] }, { "cell_type": "markdown", "metadata": { "id": "X8-tubYY04p_" }, "source": [ "## Identify the Parameters" ] }, { "cell_type": "markdown", "metadata": { "id": "PQqhvSZN04p_" }, "source": [ "We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating Optimisation Dataset\n", "\n", "The dataset for optimisation is composed of time, current, and the noisy voltage data:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "id": "zuvGHWID04p_" }, "outputs": [], "source": [ "dataset = pybop.Dataset(\n", " {\n", " \"Time [s]\": t_eval,\n", " \"Current function [A]\": values[\"Current [A]\"].data,\n", " \"Voltage [V]\": corrupt_values,\n", " }\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "ffS3CF_704qA" }, "source": [ "### Defining Parameters to Estimate\n", "\n", "We select the parameters for estimation and set up their prior distributions and bounds:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "id": "WPCybXIJ04qA" }, "outputs": [], "source": [ "parameters = [\n", " pybop.Parameter(\n", " \"Negative electrode active material volume fraction\",\n", " prior=pybop.Gaussian(0.6, 0.02),\n", " bounds=[0.5, 0.8],\n", " ),\n", " pybop.Parameter(\n", " \"Positive electrode active material volume fraction\",\n", " prior=pybop.Gaussian(0.48, 0.02),\n", " bounds=[0.4, 0.7],\n", " ),\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "n4OHa-aF04qA" }, "source": [ "### Setting up the Optimisation Problem\n", "\n", "With the datasets and parameters defined, we can set up the optimisation problem, its cost function, and the optimiser." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "id": "etMzRtx404qA" }, "outputs": [], "source": [ "problem = pybop.FittingProblem(model, parameters, dataset)\n", "cost = pybop.SumSquaredError(problem)\n", "optim = pybop.Optimisation(cost, optimiser=pybop.SciPyDifferentialEvolution)\n", "optim.set_max_iterations(400)" ] }, { "cell_type": "markdown", "metadata": { "id": "caprp-bV04qB" }, "source": [ "### Running the Optimisation\n", "\n", "We proceed to run the Differential Evolution optimisation algorithm to estimate the parameters:" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "id": "-9OVt0EQ04qB" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Ignoring x0. Initial conditions are not used for differential_evolution.\n" ] } ], "source": [ "x, final_cost = optim.run()" ] }, { "cell_type": "markdown", "metadata": { "id": "-4pZsDmS04qC" }, "source": [ "### Viewing the Estimated Parameters\n", "\n", "After the optimisation, we can examine the estimated parameter values:" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Hgz8SV4i04qC", "outputId": "e1e42ae7-5075-4c47-dd68-1b22ecc170f6" }, "outputs": [ { "data": { "text/plain": [ "array([0.74999764, 0.66481528])" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x # This will output the estimated parameters" ] }, { "cell_type": "markdown", "metadata": { "id": "KxKURtH704qC" }, "source": [ "## Plotting and Visualisation\n", "\n", "PyBOP provides various plotting utilities to visualise the results of the optimisation." ] }, { "cell_type": "markdown", "metadata": { "id": 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", 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