This repository contains the slides and code related to the following webinar:
- Soft Magnetic Materials and Ordinary Differential Equations
- From Linear Circuits to Neural Network Models
- IEEE MagNet Challenge Webinar - May 23 2025
- Thomas Guillod - Dartmouth College
This webinar focuses on ordinary differential equation models for soft-magnetic materials:
- Using equation-based models (linear and nonlinear circuits)
- Using neural ordinary differential equation models
The Python code has the following features:
- Training and inference of ODE models
- Management of the dataset with dataframes
- Various metrics and plotting capabilities
- Using JAX for computations (does not require a GPU)
- Using Diffrax for solving the ODEs (with adjoints)
Various optimizers can be used to train the models:
- Scipy / latin hypercube sampler
- Scipy / differential evolution
- JAX / Optimistix / minimize solver
- JAX / Optimistix / least-square
- JAX / Optax / gradient descent
- Main Files
- slides.pdf - Slides of the webinar (CC BY-ND 4.0)
- requirements.txt - List of the used Python packages.
- Python Files
- run_1_dataset.py - Parse the CSV files into a DataFrame.
- run_2_eqn_train.py - Train an equation-based model.
- run_3_eqn_infer.py - Inference of an equation-based model.
- run_4_ann_train.py - Train a neural network-based model.
- run_5_ann_infer.py - Inference of a neural network-based model.
- model_eqn.py - Specifications of the equation-based models.
- model_ann.py - Specifications of the neural network-based models.
- Folders and Python Packages
- The goal of this code is to demonstrate basic ODE models.
- The implementation is neither comprehensive nor optimized.
- The dataset provided in the repository is very small.
- The dataset is extracted from the MagNetX dataset.
- Tested on Linux x86/64.
- Tested with Python 3.12.3.
- Package list in
requirements.txt
.
- Name: Thomas Guillod
- Affiliation: Dartmouth College
- Email: [email protected]
- Website: https://otvam.ch
This research was done at Dartmouth College by the research group of Prof. Sullivan:
- Dartmouth College, NH, USA: https://dartmouth.edu
- Dartmouth Engineering: https://engineering.dartmouth.edu
(c) 2024-2025 / Thomas Guillod / Dartmouth College
This Source Code Form is subject to the terms of the Mozilla Public License, v. 2.0. If a copy of the MPL was not distributed with this file, You can obtain one at http://mozilla.org/MPL/2.0/.