jinns: a JAX Library for Physics-Informed Neural Networks

• URL: http://arxiv.org/abs/2412.14132v1
• Date: Wed, 18 Dec 2024 18:21:41 GMT
• Title: jinns: a JAX Library for Physics-Informed Neural Networks
• Authors: Hugo Gangloff, Nicolas Jouvin,
• Abstract summary: jinns is an open-source Python library for physics-informed neural networks. Rooted in the JAX ecosystem, it provides a versatile framework for efficiently prototyping real-problems.
• Score: 0.7366405857677227
• License:
• Abstract: 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.

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