Merge pull request #316 from pybop-team/295-add-adamw-optimiser

Adds AdamW optimiser

CHANGELOG.md

@@ -2,6 +2,8 @@
 
 ## Features
 
+
+- [#316](https://github.com/pybop-team/PyBOP/pull/316) - Adds Adam with weight decay (AdamW) optimiser, adds depreciation warning for pints.Adam implementation.
 - [#271](https://github.com/pybop-team/PyBOP/issues/271) - Aligns the output of the optimisers via a generalisation of Result class.
 - [#315](https://github.com/pybop-team/PyBOP/pull/315) - Updates __init__ structure to remove circular import issues and minimises dependancy imports across codebase for faster PyBOP module import. Adds type-hints to BaseModel and refactors rebuild parameter variables.
 - [#236](https://github.com/pybop-team/PyBOP/issues/236) - Restructures the optimiser classes, adds a new optimisation API through direct construction and keyword arguments, and fixes the setting of `max_iterations`, and `_minimising`. Introduces `pybop.BaseOptimiser`, `pybop.BasePintsOptimiser`, and `pybop.BaseSciPyOptimiser` classes.

README.md

@@ -85,13 +85,13 @@ The table below lists the currently supported [models](https://github.com/pybop-
 |-----------------------------------------------|-------------------------------------------------------------|------------------------------------------|
 | Single Particle Model (SPM) | Covariance Matrix Adaptation Evolution Strategy (CMA-ES) | Sum of Squared Errors (SSE) <tr></tr> |
 | Single Particle Model with Electrolyte (SPMe) | Particle Swarm Optimization (PSO) | Root Mean Squared Error (RMSE) <tr></tr> |
-| Doyle-Fuller-Newman (DFN) | Adaptive Moment Estimation (Adam) | Maximum Likelihood Estimation (MLE) <tr></tr> |
-| Many Particle Model (MPM) | Improved Resilient Backpropagation (iRProp-) | Maximum a Posteriori (MAP) <tr></tr> |
-| Multi-Species Multi-Reactants (MSMR) | Exponential Natural Evolution Strategy (xNES) | Unscented Kalman Filter (UKF) <tr></tr> |
-| Equivalent Circuit Models (ECM) | Separable Natural Evolution Strategy (sNES) | Gravimetric Energy Density <tr></tr> |
-| | Gradient Descent | Volumetric Energy Density <tr></tr> |
+| Doyle-Fuller-Newman (DFN) | Exponential Natural Evolution Strategy (xNES)  | Gaussian Log Likelihood <tr></tr> |
+| Many Particle Model (MPM) | Separable Natural Evolution Strategy (sNES) | Gaussian Log Likelihood w/ known variance <tr></tr> |
+| Multi-Species Multi-Reactants (MSMR) | Adaptive Moment Estimation with Weight Decay (AdamW) | Maximum a Posteriori (MAP) <tr></tr> |
+| Equivalent Circuit Models (ECM) | Improved Resilient Backpropagation (iRProp-) | Unscented Kalman Filter (UKF) <tr></tr> |
+| | SciPy Minimize & Differential Evolution | Gravimetric Energy Density <tr></tr> |
+| | Gradient Descent| Volumetric Energy Density<tr></tr> |
 | | Nelder-Mead | <tr></tr> |
-| | SciPy Minimize & Differential Evolution | <tr></tr> |
 
 </p>
 

benchmarks/benchmark_parameterisation.py

@@ -13,7 +13,7 @@ class BenchmarkParameterisation:
         [
             pybop.SciPyMinimize,
             pybop.SciPyDifferentialEvolution,
-            pybop.Adam,
+            pybop.AdamW,
             pybop.CMAES,
             pybop.GradientDescent,
             pybop.IRPropMin,

benchmarks/benchmark_track_parameterisation.py

@@ -13,7 +13,7 @@ class BenchmarkTrackParameterisation:
         [
             pybop.SciPyMinimize,
             pybop.SciPyDifferentialEvolution,
-            pybop.Adam,
+            pybop.AdamW,
             pybop.CMAES,
             pybop.GradientDescent,
             pybop.IRPropMin,