main.tex

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\title{3rd Online Developer Meeting}
\titlegraphic{\includegraphics[width=6cm]{Theme/Logos/PyBOP_logo.png}}
\author{The PyBOP Team \texorpdfstring{\href{https://github.com/pybop-team/PyBOP}{\faGithub}}{}}
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\date{\today}

\begin{document}

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% \section*{Outline}
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    \begin{beamercolorbox}[sep=8pt,center,shadow=true,rounded=true]{title}
    \usebeamerfont{title}{Agenda}\par%
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    \vspace{6mm} \normalsize
    \begin{itemize}
        \item Presentation on recent updates to PyBOP (Nicola, 10 min)
        \item Quick introduction/update from everyone (5 min)
        \item Discussion of open issues (30 min)
        \item Planning and assignment of tasks (15 min)
    \end{itemize}
    \end{beamercolorbox}
    Introductory slides are available at the end of this presentation.
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\begin{frame}{PyBOP project map}
    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[height=0.42\textwidth]{Images/Diagrams/PyBOP-high-level.png}
        \label{fig:project_map}
    \end{figure}
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\section{Updates}

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    \centering
    \begin{beamercolorbox}[sep=8pt,center,shadow=true,rounded=true]{title}
    \usebeamerfont{title}{Recent updates}\par%
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    \vspace{6mm} \normalsize
    We released PyBOP v24.6 on 8th July. \\
    \vspace{6mm}
    \href{https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md}{https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md} \\
    \vspace{6mm}
    The Changelog is divided into new \textbf{features},  \textbf{bug fixes} and \textbf{breaking changes}.
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\begin{frame}[fragile,t]{New in v24.6: Additional optimisers}
    \vspace{-6mm}
    \issue{319} - Adds CuckooSearch optimiser with corresponding tests.
    \issue{316} - Adds Adam with weight decay (AdamW), deprecates pints.Adam.

    \begin{table}[]
        \centering
        \footnotesize
    \begin{tabularx}{0.86\textwidth}{*{2}{Y}}
         Adam with weight decay (AdamW) &
         Cuckoo search
    \end{tabularx}
    \end{table}
    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.2\textwidth]{Images/Optimisers/adamw_cost.png}
        \includegraphics[width=0.28\textwidth]{Images/Optimisers/adamw_params.png} ~
        \includegraphics[width=0.2\textwidth]{Images/Optimisers/cuckoo_cost.png}
        \includegraphics[width=0.28\textwidth]{Images/Optimisers/cuckoo_params.png}
        % \caption{Caption}
        \label{fig:optimisersNew}
    \end{figure}
\end{frame}

\begin{frame}[fragile,t]{New in v24.6: GITT fitting example}
    \vspace{-6mm}
    \issue{249} - Add WeppnerHuggins model and GITT example.

    \begin{figure}
        \centering
        \includegraphics[width=0.5\textwidth]{Images/Highlights/gitt_IRPropMin_fit.png}
        \includegraphics[width=0.48\textwidth]{Images/Highlights/gitt_IRPropMin_parameter.png}
        % \caption{Caption}
        \label{fig:WeppnerHuggins}
    \end{figure}
\end{frame}

\begin{frame}[fragile,t]{New feature (WIP): Multi-fitting problem class}
    \vspace{-6mm}
    \issue{364} - Adds the MultiFittingProblem class and the multi\_fitting example script.
    \issue{322} - Add Parameters class to store and access multiple parameters in one object.

    \vspace{-2mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.3\textwidth]{Images/Highlights/multi_data_fitting.png}
        \includegraphics[width=0.34\textwidth]{Images/Highlights/multi_cost.png}
        \includegraphics[width=0.34\textwidth]{Images/Highlights/multi_parameters.png}
        % \caption{Caption}
        \label{fig:MultiFitting}
    \end{figure}
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    \centering
    \begin{beamercolorbox}[sep=8pt,center,shadow=true,rounded=true]{title}
    \usebeamerfont{title}{Questions?}\par%
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\section{Discussion}

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    \centering
    \begin{beamercolorbox}[sep=8pt,center,shadow=true,rounded=true]{title}
    \usebeamerfont{title}{Discussion of open issues}\par%
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    \LARGE{\faUserPlus} \LARGE{\faBug} \\
    \normalsize
    \begin{flushleft}
        Let's introduce everyone:
    \end{flushleft}
    \begin{itemize}
        \item Where are you joining from?
        \item What issue are you working on/would you like to work on?
    \end{itemize}
    \vspace{6mm} \normalsize
    Open issues: \href{https://github.com/pybop-team/PyBOP/issues}{https://github.com/pybop-team/PyBOP/issues} \\
    Project board: \href{https://github.com/orgs/pybop-team/projects/7}{https://github.com/orgs/pybop-team/projects/7}
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%%%%%%%%%%%%%%%%%%%%%%
%%%% INTRODUCTION %%%%
%%%%%%%%%%%%%%%%%%%%%%

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    {\color{white}\usebeamerfont{title}{End of meeting}\par}%
    \color{white}\noindent\rule{10cm}{1pt} \\
    {\color{white}\usebeamerfont{title}{Below: \\
    1. Introductory slides \\
    2. Feature highlights }\par}%
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\section{Introduction}
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    \vspace{6mm} \normalsize
    \begin{flushleft}
        With thanks to:
    \end{flushleft}
    \begin{itemize}
        \item the PyBaMM developers \href{https://github.com/pybop-team/PyBOP}{\includegraphics[height=1.2em]{Theme/Logos/PyBaMM_logo.png}}
        \item the PINTS developers \href{https://github.com/pints-team/pints}{\faGithub}
    \end{itemize}
\end{frame}


\begin{frame}{Aims}
    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=\textwidth]{Images/Diagrams/ProblemTypes.png}
    \end{figure}
\end{frame}

\begin{frame}{Architecture}
    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.9\textwidth]{Images/Diagrams/pybop_architecture.png}
        % \caption{Caption}
        \label{fig:architecture}
    \end{figure}
\end{frame}

\begin{frame}{Python objects}
    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.95\textwidth, trim={0 10cm 0 0},clip]{Images/Diagrams/pybop_objects.png}
        % \caption{Caption}
        \label{fig:objects}
    \end{figure}
\end{frame}


\section{Demo/examples}
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    \normalsize
    \begin{itemize}
        \item Parameterisation
        \item Design optimisation
    \end{itemize}
    \end{beamercolorbox}
\end{frame}

\subsection{Parameterisation example}
\begin{frame}[fragile,t]{Parameterisation}
    \vspace{-0.5cm}
    \begin{block}{1. Define a model (PyBaMM's SPM) and fitting parameters}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
import pybop
import numpy as np

# Define model
parameter_set = pybop.ParameterSet.pybamm("Chen2020")
model = pybop.lithium_ion.SPM(parameter_set=parameter_set)

# Fitting parameters
parameters = pybop.Parameters(
    pybop.Parameter(
        "Negative particle radius [m]",
        prior=pybop.Gaussian(6e-06, 0.1e-6),
        bounds=[1e-6, 9e-6],
    ),
    pybop.Parameter(
        "Positive particle radius [m]",
        prior=pybop.Gaussian(4.5e-06, 0.1e-6),
        bounds=[1e-6, 9e-6],
    ),
)
    \end{lstlisting}
    \end{block}
\end{frame}

\subsection{Parameterisation example}
\begin{frame}[fragile,t]{Parameterisation}
    \vspace{-0.5cm}
    \begin{block}{2. Generate synthetic data and define problem, cost and optimisation}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
# Generate data
sigma = 0.001
t_eval = np.arange(0, 900, 3)
values = model.predict(t_eval=t_eval)
corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval))

# Form dataset
dataset = pybop.Dataset(
    {
        "Time [s]": t_eval,
        "Current function [A]": values["Current [A]"].data,
        "Voltage [V]": corrupt_values,
    }
)

# Generate problem, cost function, and optimisation class
problem = pybop.FittingProblem(model, parameters, dataset)
cost = pybop.SumSquaredError(problem)
optim = pybop.CMAES(cost, max_iterations=100)
    \end{lstlisting}
    \end{block}
\end{frame}

\subsection{Parameterisation example}
\begin{frame}[fragile,t]{Parameterisation}
    \vspace{-1.5cm}
    \begin{block}{3. Run the optimisation and plot the results}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
# Run the optimisation
x, final_cost = optim.run()
print("True parameters:", parameters.true_value())
print("Estimated parameters:", x)

# Plot the timeseries output
pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison")

# Plot convergence and parameter traces
pybop.plot_convergence(optim)

# Plot the parameter traces
pybop.plot_parameters(optim)

# Plot the cost landscape with optimisation path
pybop.plot2d(optim, steps=15)
    \end{lstlisting}
    \end{block}
\end{frame}

\begin{frame}{Parameterisation results}
    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.4\textwidth, trim={0 0 0 0},clip]{Images/Examples/CMAES_quick_plot.png}
        \includegraphics[width=0.4\textwidth, trim={0 0 0 0},clip]{Images/Examples/CMAES_convergence.png} \\
        \includegraphics[width=0.5\textwidth, trim={0 0 0 0},clip]{Images/Examples/CMAES_parameters.png}
        \hspace{1cm}\includegraphics[width=0.29\textwidth, trim={0 0 0 0},clip]{Images/Examples/CMAES_cost_trace.png}
        \hspace{1cm}
        \label{fig:parameterisation}
    \end{figure}
\end{frame}

\subsection{Design optimisation example}
\begin{frame}[fragile,t]{Design optimisation}
    \vspace{-1.5cm}
    \begin{block}{1. Define a model (PyBaMM's SPMe) and design parameters}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
import pybop
import numpy as np

# Define parameter set and model
parameter_set = pybop.ParameterSet.pybamm("Chen2020", formation_concentrations=True)
model = pybop.lithium_ion.SPMe(parameter_set=parameter_set)

# Fitting parameters
parameters = pybop.Parameters(
    pybop.Parameter(
        "Positive electrode thickness [m]",
        prior=pybop.Gaussian(7.56e-05, 0.05e-05),
        bounds=[65e-06, 10e-05],
    ),
    pybop.Parameter(
        "Positive particle radius [m]",
        prior=pybop.Gaussian(5.22e-06, 0.05e-06),
        bounds=[2e-06, 9e-06],
    ),
)
    \end{lstlisting}
    \end{block}
\end{frame}

\begin{frame}[fragile,t]{Design optimisation}
    \vspace{-1cm}
    \begin{block}{2. Set the target experiment and define problem, cost and optimisation}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
# Define test protocol
experiment = pybop.Experiment(
    ["Discharge at 1C until 2.5 V (5 seconds period)"],
)
init_soc = 1  # start from full charge
signal = ["Voltage [V]", "Current [A]"]

# Generate problem
problem = pybop.DesignProblem(
    model, parameters, experiment, signal=signal, init_soc=init_soc
)

# Generate cost function and optimisation class:
cost = pybop.GravimetricEnergyDensity(problem, update_capacity=True)
optim = pybop.PSO(
    cost, verbose=True, allow_infeasible_solutions=False, max_iterations=15
)
    \end{lstlisting}
    \end{block}
\end{frame}

\begin{frame}[fragile,t]{Design optimisation}
    \vspace{-2cm}
    \begin{block}{3. Run the optimisation and plot the results}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
# Run optimisation
x, final_cost = optim.run()
print("Estimated parameters:", x)
print(f"Initial gravimetric energy density: {cost(optim.x0):.2f} Wh.kg-1")
print(f"Optimised gravimetric energy density: {final_cost:.2f} Wh.kg-1")

# Plot the timeseries output
if cost.update_capacity:
    problem._model.approximate_capacity(x)
pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison")

# Plot the cost landscape with optimisation path
pybop.plot2d(optim, steps=10)
    \end{lstlisting}
    \end{block}
\end{frame}

\begin{frame}[fragile,t]{Design optimisation}
    \vspace{-6mm}
    \begin{block}{Output}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
Estimated parameters: [6.51159429e-05 2.32340723e-06]
Initial gravimetric energy density: 386.31 Wh.kg-1
Optimised gravimetric energy density: 410.78 Wh.kg-1
    \end{lstlisting}
    \end{block}
    Results
    \vspace{-3mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.59\textwidth, trim={0 0 0 0},clip]{Images/Examples/MaxEnergy_quick_plot.png}
        \includegraphics[width=0.29\textwidth, trim={0 0 0 0},clip]{Images/Examples/MaxEnergy_cost_trace.png} \\
        \label{fig:design_optimisation}
    \end{figure}
\end{frame}


\section{Choice of optimisers}
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\subsection{Gradient-based optimisers}
\begin{frame}{Gradient-based}
    \vspace{-6mm}
    \begin{table}[]
        \centering
        \footnotesize
    \begin{tabularx}{0.86\textwidth}{*{3}{Y}}
         Gradient descent &
         Adaptive moment (AdamW) &
         Resilient backpropagation (IRPropMin)
    \end{tabularx}
    \end{table}

    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/graddesc_cost.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/adamw_cost.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/irpropmin_cost.png} ~~~\\
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/graddesc_params.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/adamw_params.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/irpropmin_params.png}
        % \caption{Caption}
        \label{fig:optimisers1}
    \end{figure}
\end{frame}

\subsection{Non-gradient-based optimisers}
\begin{frame}{Evolution strategies}
    \vspace{-5mm}
    \begin{table}[]
        \centering
        \footnotesize
    \begin{tabularx}{0.86\textwidth}{*{3}{Y}}
         Stochastic natural evolution strategy (SNES) &
         Exponential natural evolution strategy (SNES) &
         Covariance matrix adaptation (CMA-ES)
    \end{tabularx}
    \end{table}

    \vspace{-5mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/snes_cost.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/xnes_cost.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/cmaes_cost.png} ~~~\\
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/snes_params.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/xnes_params.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/cmaes_params.png}
        % \caption{Caption}
        \label{fig:optimisers4}
    \end{figure}
\end{frame}

\begin{frame}{Other optimisers}
    \vspace{-6mm}
    \begin{table}[]
        \centering
        \footnotesize
    \begin{tabularx}{\textwidth}{*{4}{Y}}
         Nelder Mead &
         Particle swarm optimisation &
         SciPy differential evolution
    \end{tabularx}
    \end{table}

    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/neldermead_cost.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/pso_cost.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/scipyde_cost.png} ~~~~\\
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/neldermead_params.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/pso_params.png}
        \includegraphics[width=0.24\textwidth]{Images/Optimisers/scipyde_params.png}
        \label{fig:optimisers2}
    \end{figure}
\end{frame}

\begin{frame}{Collaboration}
    \vspace{-6mm}
    \begin{figure}
        \centering
        \includegraphics[width=\textwidth]{Images/Code_of_Conduct.png}
    \end{figure}
\end{frame}

\section{Feature highlights}
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    \centering
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    \color{oxfordblue}\noindent\rule{10cm}{1pt} \\
    \LARGE{\faBatteryThreeQuarters} \\
    \end{beamercolorbox}
\end{frame}

\begin{frame}[fragile,t]{New in v24.6: Experimental data fitting}
    \vspace{-6mm}
    \issue{241} - Adds experimental circuit model fitting notebook with LG M50 data from: \href{https://github.com/WDWidanage/Simscape-Battery-Library/tree/main/Examples/parameterEstimation_TECMD/Data}{https://github.com/WDWidanage/Simscape-Battery-Library/tree/main/Examples/parameterEstimation\_TECMD/Data}
    \begin{figure}
        \centering
        \includegraphics[height=0.3\textwidth]{Images/Highlights/LGM50_Fig1.png}
        \includegraphics[height=0.3\textwidth]{Images/Highlights/LGM50_Fig2.png}
        \label{fig:LGM50}
    \end{figure}
\end{frame}

\begin{frame}{New in v24.6: Additional PyBaMM models}
    \vspace{-6mm}
    \issue{250} - Adds DFN, MPM, MSMR models and moves multiple construction variables to BaseEChem. Adds exception catch on simulate \& simulateS1.
    \begin{figure}
        \centering
        \includegraphics[height=0.35\textwidth]{Images/Diagrams/Supported_classes.png}
        \label{fig:supported_classes}
    \end{figure}
\end{frame}

\begin{frame}{New in v24.6: Maximum a Posteriori}
    \vspace{-6mm}
    \issue{275} - Adds Maximum a Posteriori (MAP) cost function with corresponding tests and example script.
    \begin{figure}
        \centering
        \includegraphics[height=0.35\textwidth]{Images/Highlights/MAP_param_convergence.png}
        \includegraphics[height=0.35\textwidth]{Images/Highlights/MAP_cost_landscape.png}
        \label{fig:MAP_cost}
    \end{figure}
\end{frame}

\begin{frame}[fragile,t]{New in v24.6: Testing and infrastructure}
    \vspace{-6mm}
    Credit and thanks to Agriya and Brady!
    \begin{block}{Features}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
#304 - Decreases the testing suite completion time.
#301 - Updates default echem solver to "fast with events" mode.
#251 - Increment PyBaMM > v23.5, remove redundant tests within integration tests, increment citation version, fix examples with incorrect model definitions.
#285 - Drop support for Python 3.8.
#268 - Fixes the GitHub Release artifact uploads, allowing verification of codesigned binaries and source distributions via sigstore-python.
#79 - Adds BPX as a dependency and imports BPX support from PyBaMM.
#267 - Add classifiers to pyproject.toml, update project.urls.
#195 - Adds the Nelder-Mead optimiser from PINTS as another option.
    \end{lstlisting}
    \end{block}
    \begin{block}{Bug Fixes}
    \begin{lstlisting}[firstnumber=1, xleftmargin=10pt]
#317 - Installs seed packages into nox sessions, ensuring that scheduled tests can pass.
#308 - Enables testing on both macOS Intel and macOS ARM (Silicon) runners and fixes the scheduled tests.
#299 - Bugfix multiprocessing support for Linux, MacOS, Windows (WSL) and improves coverage.
#270 - Updates PR template.
#91 - Adds a check on the number of parameters for CMAES and makes XNES the default optimiser.
    \end{lstlisting}
    \end{block}
\end{frame}

\end{document}