jinns is an open-source Python library for physics-informed neural networks, built to tackle both forward and inverse problems, as well as meta-model learning. Rooted in the JAX ecosystem, it provides a versatile framework for efficiently prototyping real-problems, while easily allowing extensions to specific needs. Furthermore, the implementation leverages existing popular JAX libraries such as equinox and optax for model definition and optimisation, bringing a sense of familiarity to the user. Many models are available as baselines, and the documentation provides reference implementations of different use-cases along with step-by-step tutorials for extensions to specific needs. The code is available on Gitlab https://gitlab.com/mia_jinns/jinns.
Cite
@article{arxiv.2412.14132,
title = {jinns: a JAX Library for Physics-Informed Neural Networks},
author = {Hugo Gangloff and Nicolas Jouvin},
journal= {arXiv preprint arXiv:2412.14132},
year = {2024}
}