v26.3

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v26.3 - 2026-03-05

Features

• #897 - Adds separate LogPrior, LogPDF and LogPosterior classes and updates set_target.
• #873 - Adds methods for saving result and reconstructing result from saved data. result.save: saves entire python object using pickle. result.save_data: saves primarily the logger data and any other data required to reconstruct the result from the problem or the sampler (for SamplingResult). Result.load_result: reconstructs the Result object based on the underlying problem (or sampler for SamplingResult) and the data saved to file.
• #862 - Adds pybop.MarginalDistribution, pybop.MultivariateLogNormal.
• #889 - Adds methods for setting the initial state from a voltage to the grouped models.
• #869 - Adds methods for pre-processing current data for linear interpolation.
• #868 - Adds support for Python3.13 (NumPy restricted to <2.4, EP-BOLFI optimiser and PyProBE do not support Python 3.13).
• #871 - Adds a lumped thermal model called CellTemperature.
• #846 - Adds Bayesian optimisation framework and, as an example, the EP-BOLFI optimiser

Bug Fixes

• #890 - Fix the assignment of parameters within a MetaProblem.
• #847 - Update readme and diagram of pybop components so that the diagram is displayed correctly in the readme.

Breaking Changes

• #894 - Distinguish different uses of sigma, pass the covariance to the samplers, and add parameter get_mean and get_std functions.
• #862 - Removes MultivariateParameters class. Instead allows multivariate parameters to be passed via pybamm.ParameterValues (as a pybop.Parameter with a pybop.MarginalDistribution). The pybop.Parameters class now handles multivariate parameters. Multivariate distributions are now defined in the model space instead of the search space.
• #878 - Use "Current [A]" instead of "Current function [A]" in datasets and allow list of control functions.
• #864 - Remove check_already_exists from ParameterValues following PyBaMM PR 5339.
• #860 - Create a parent class for optimisation and sampling results, move PosteriorSummary attributes to the SamplingResult and deprecate the pints.AdaptiveCovarianceMCMC sampler.
• #857 - Deprecate the custom PyBaMM model build process for a simulation without an experiment and rename batch_solve as solve_batch to align with other functions.
• #839 - Renames 'prior' as 'distribution' for pybop.Parameter. Allows construction of a pybop.Parameter with a distribution of type scipy.stats.distributions.rv_frozen. Removes margins, set_bounds, remove_bounds from pybop.Parameter.

v25.11

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v25.11 - 2025-11-24

Features

• #815 - Adds function import_pyprobe_result to import a pyprobe.result into a pybop.dataset. Allows for creating a dataset directly from a pybamm.solution object.

Optimisations

• #837 - Update the descriptons in the example scripts and notebooks.
• #833 - Upgrade to Pints 0.5.1, PyBaMM 25.10.1 and NumPy 2, fix some deprecation warnings.
• #816 - Enable simulator multi-processing via the evaluators.

Breaking Changes

• #829 - Create SamplingResult and best inputs property for results.

v25.10

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v25.10 - 2025-10-31

This release presents a major restructure of PyBOP's base classes. We move from setting up a model, problem, cost,
then optimiser to defining a simulator, cost, problem, and then optimiser. A pybop.pybamm.Simulator is designed
to simulate a pybamm.BaseModel. Optimisation parameters can be passed through a pybamm.ParameterValues class.
To understand how to update your use of PyBOP, please take a look at the example notebooks and scripts.

Breaking Changes

• #820 - Remove the name property from pybop.Parameter
• #821 - Remove the papers folder and update Readme.
• #809 - Major restructure, including:
• Deprecate Python 3.9 support
• Update initial state setting (requires PyBaMM > 25.8)
• Remove jax methods
• Add PyBaMM and PyBaMM-EIS simulators for rebuilding and running simulations for a given set of input parameters
• Remove PyBaMM wrappers and enable use of PyBaMM model, parameter values and experiment classes
• Remove observers
• Remove standalone class examples
• Improve logging
• Remove Optimisation and MCMCSampler wrapper classes
• Remove Fisher information computation
• Rename apply_transform argument to transformed
• Remove the update_capacity option from the DesignProblem
• Update sensitivities retrieval (for PyBaMM 25.8)
• Remove uninformative examples
• Move optimiser and sampler options into defined classes
• Add PyBaMM utilities, design variable definitions and the add_variable_to_model function
• Allow plotting via functions on the OptimisationResult
• Separate the cost classes from the Problem
• Replace FittingProblem and DesignProblem by a single Problem class
• Rename and reimplement MultiFittingProblem as MetaProblem
• Add BaseSimulator as a generic base class for the pybop.pybamm.Simulator and pybop.pybamm.EISSimulator
• Enable pybop.Parameter objects to be passed directly to an instance of pybamm.ParameterValues
• Update the method for setting formation concentrations to be part of the model definition
• Rename some example scripts and notebooks
• Update the docs and test workflows

v25.6

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v25.6 - 2025-07-16

Features

• #767 - Adds the GroupedSPM model for parameter identification.
• #644 - Adds example applications for common battery experiments.

Optimisations

• #763 - Updates the GITT pulse fitting method to allow iteration over many pulses.

Bug Fixes

• #771 - Match naming of n_sensitivity_samples and fix intermittent test_optimisation_f_guessed test.
• #737 - Sensitivities no longer available for CasadiSolver in Pybamm v25.6 onwards. Updates Hallemans example scripts.
• #705 - Bug fix fitting_problem.evaulate() failure return type alongside fixes for Pybamm v25.4.
• #546 - Default Pybamm solver to IDAKLU, changes required for Pybamm v25.4.1

v25.3

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v25.3 - 2025-03-28

Features

• #649 - Adds verbose outputs to Pints-based optimisers
• #659 - Enables user-defined weightings of the error measures.
• #674 - Adds the reason for stopping to the OptimisationResult.
• #663 - Adds DFN fitting examples alongside synthetic data generation methods.

Optimisations

• #676 - Update the format of the problem sensitivities to a dict.
• #681 - Update the spatial variable defaults of the GroupedSPMe model.
• #692 - Improvements/fixes for BaseSampler and BasePintsSampler classes, adds ChainProcessor classes w/ clearer structure.

Bug Fixes

• #678 - Fixed bug where model wasn't plotted for observer classes with pybop.plot.quick().

Breaking Changes

• #684 - Updates plot.quick to plot.problem for clarity.
• #661 - Adds pybop.CostInterface which aligns the optimisers and samplers with a unified call_cost in which transformations and sign inversions are applied. Also includes bug fixes for transformations and gradient calculations.

v25.1

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v25.1 - 2025-02-03

Features

• #636 - Adds pybop.IRPropPlus optimiser with corresponding tests.
• #635 - Adds support for multi-proposal evaluation of list-like objects to BaseCost classes.
• #635 - Adds global parameter sensitivity analysis with method BaseCost.sensitivity_analysis. This is computation is added to OptimisationResult if optimiser arg compute_sensitivities is True. An additional arg is added to select the number of samples for analysis: n_sensitivity_samples.
• [#630] (#632) - Fisher Information Matrix added to BaseLikelihood class.
• #619 - Adds pybop.SimulatingAnnealing optimiser with corresponding tests.
• #565 - DigiBatt added as funding partner.

Optimisations

• #638 - Allows the problem class to accept any domain name.
• #618 - Adds Mean Absolute Error (MAE) and Mean Squared Error (MSE) costs.
• #601 - Deprecates MultiOptimisationResult by merging with OptimisationResult.
• #600 - Removes repetitious functionality within the cost classes.

Bug Fixes

• #602 - Aligns the standard quick plot of MultiFittingProblem outputs.

Breaking Changes

• #656 - Completes ParameterSet changes from #593 and aligns the simulation options in model.predict with the model properties such as the solver.
• #593 - Enables ParameterSet to systematically return a pybamm.ParameterValues object within the model class.

New Contributors

• @pipliggins made their first contribution in #573

v24.12

Full Changelog

https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

What's Changed

• v24.9.1 main -> develop by @BradyPlanden in #511
• JuPyter notebook link in README.md file broke because of file structure change by @Dibyendu-IITKGP in #509
• LogPosterior refactor, partial fix for #510 by @BradyPlanden in #512
• chore: update pre-commit hooks by @pre-commit-ci in #516
• bpx is added as an optional dependency by @Dibyendu-IITKGP in #499
• Adds positional args for nox tests session by @BradyPlanden in #519
• chore: update pre-commit hooks by @pre-commit-ci in #523
• Adds transformations notebook by @BradyPlanden in #521
• chore: update pre-commit hooks by @pre-commit-ci in #528
• Add PowerDensity costs by @NicolaCourtier in #530
• Align notebook names by @BradyPlanden in #534
• Refactors OptimisationResult class, adds finite cost check for identified parameters by @BradyPlanden in #527
• Allow linked design parameters by @NicolaCourtier in #533
• Adds Voronoi cost landscape plot by @BradyPlanden in #531
• chore: update pre-commit hooks by @pre-commit-ci in #537
• OptimiseResult().str across examples, adds OCP balancing script by @BradyPlanden in #536
• Allow different lengths for validation plots by @NicolaCourtier in #538
• Adds plotting methods for monte carlo samplers by @BradyPlanden in #409
• chore: update pre-commit hooks by @pre-commit-ci in #539
• Adds ScaledLogLikelihood Class by @BradyPlanden in #541
• Define StandardPlot add_traces and parse_data by @NicolaCourtier in #545
• Update mass and capacity calculations for half-cell models by @NicolaCourtier in #543
• chore: update pre-commit hooks by @pre-commit-ci in #547
• chore: update pre-commit hooks by @pre-commit-ci in #552
• Refactor optimisation args by @BradyPlanden in #551
• Additions to #551 by @BradyPlanden in #555
• Add ECM example with change of parameters by @NicolaCourtier in #557
• chore: update pre-commit hooks by @pre-commit-ci in #558
• Scheduled tests, lychee, default bounds by @BradyPlanden in #562
• Divide examples into folders by @NicolaCourtier in #559
• Update readme by @NicolaCourtier in #564
• chore: update pre-commit hooks by @pre-commit-ci in #568
• Parameter specific learning rates for Gradient Descent by @BradyPlanden in #569
• Unit Hypercube Transformation by @BradyPlanden in #566
• Fix bug with old PyBaMM version being fetched for scheduled tests by @agriyakhetarpal in #572
• chore: update pre-commit hooks by @pre-commit-ci in #574
• Improvement to landscape plots, MCMC summary, x0 log entry by @BradyPlanden in #570
• Update notebooks by @BradyPlanden in #575
• Fixes security risk with download-artifact action by @BradyPlanden in #576
• Fixes #581 - population_size setter by @BradyPlanden in #582
• Add naming utility by @NicolaCourtier in #579
• Multi-start functionality for optimisers by @BradyPlanden in #571
• Fix SciPy cost logs by @NicolaCourtier in #561
• chore: update pre-commit hooks by @pre-commit-ci in #589
• Fix tests on example scripts by @NicolaCourtier in #586
• Scale points for Voronoi plots by @NicolaCourtier in #587
• Add GroupedSPMe model by @NicolaCourtier in #584
• Bug fix GroupedSPMe to enable experiments by @NicolaCourtier in #592
• Fixes non-finite LogTransformed parameter bounds by @BradyPlanden in #595
• Adds ExponentialDecay Model by @BradyPlanden in #583
• chore: update pre-commit hooks by @pre-commit-ci in #599
• Removes depreciated Adam optimiser by @BradyPlanden in #598
• Set minimising for each cost and make a property of BaseOptimiser only by @NicolaCourtier in #588
• Add Hessian check for 2-parameter problems by @NicolaCourtier in #363
• Evaluations in OptimisationResult by @BradyPlanden in #597
• random_search optimiser added in the pints framework by @Dibyendu-IITKGP in #580
• Adds JAX IDAKLU solver integration by @BradyPlanden in #481
• docs: add Dibyendu-IITKGP as a contributor for test by @allcontributors in #606

v24.9.1

What's Changed

Patch release for yanked v24.9.0.

• Shift scheduled test workflow by @BradyPlanden in #496
• Updates to README.md by @BradyPlanden in #501
• Transformation fixes by @BradyPlanden in #495
• Notebook example files added for ECM and folder structure updated. by @Dibyendu-IITKGP in #460
• docs: add Dibyendu-IITKGP as a contributor for example by @allcontributors in #507
• Fixes for LogPosterior w/ GaussianLogLikelihood by @BradyPlanden in #505

New Contributors

• @Dibyendu-IITKGP made their first contribution in #460

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v24.9.0

What's Changed

• Add Lychee link checking by @BradyPlanden in #403
• Additions to ruff rules by @BradyPlanden in #386
• Adds Minkowski and SumofPower Cost by @BradyPlanden in #383
• Add a WeightedCost by @NicolaCourtier in #329
• Adds Transformation class by @BradyPlanden in #357
• Additions for #327 - Enable DesignCosts in WeightedCost by @BradyPlanden in #413
• Add get_parameter_info to BaseModel by @NicolaCourtier in #423
• Update default init_soc for DesignProblem by @NicolaCourtier in #422
• Add model.clear and remove setters by @NicolaCourtier in #448
• Fix parameter set logic and allow FittingProblem to take init_ocv rather than init_soc by @NicolaCourtier in #425
• Merge model build and rebuild by @NicolaCourtier in #451
• Add MultiFittingProblem class and example by @NicolaCourtier in #364
• Adds support for IDAKLU w/ output variables by @BradyPlanden in #450
• Fixes benchmarks by @BradyPlanden in #457
• Refactors BaseCost.evaluate/S1 into BaseCost.compute by @BradyPlanden in #455
• Adds EIS prediction methods by @BradyPlanden in #405
• Add functional parameter example by @NicolaCourtier in #442
• Rename quick_plot.py to standard_plots.py by @NicolaCourtier in #469
• Add electrode balancing example by @brosaplanella in #472
• Allow nonlinear constraints #353 by @MarkBlyth in #371
• Updates type hints in ecm_tau_constraints.py by @BradyPlanden in #480
• Adds Monte Carlo Samplers by @BradyPlanden in #340
• Adds cost/likelihood class notebook by @BradyPlanden in #470
• Fixes for PyBaMM v24.9 dependency by @BradyPlanden in #485
• Converts notebook plots to pure Plotly by @BradyPlanden in #411
• Refactor update_capacity by @NicolaCourtier in #465
• Create creating_a_model.ipynb by @f-g-r-i-m-m in #471
• Replace pybop.MAP with pybop.LogPosterior by @BradyPlanden in #483
• Multi-dimensional learning rate (sigma0) for AdamW by @BradyPlanden in #462
• Fix transformations by @NicolaCourtier in #474

New Contributors

• @f-g-r-i-m-m made their first contribution in #471

Full Changelog: https://github.com/pybop-team/PyBOP/blob/develop/CHANGELOG.md

v24.6.1

What's Changed

• Release v24.6.1 by @BradyPlanden in #415
• Increments supported PyBaMM version to >= v24.5

Full Changelog: https://github.com/pybop-team/PyBOP/blob/main/CHANGELOG.md#v2461---2024-07-31