JAX开源工具箱，助力物理信息神经网络快速建模与求解。
jinns: a JAX Library for Physics-Informed Neural Networks
- 基于JAX生态，整合equinox和optax，支持前向与反向问题求解。
- 提供多种模型基准与分步教程，便于扩展至具体应用。
- 适合需要高效构建物理约束神经网络的研究者与工程师。
jinns 是一个面向物理信息神经网络的开源Python库，旨在解决正向与反向问题以及元建模学习。其根植于JAX生态系统，提供灵活高效的原型设计框架，并可轻松拓展以满足特定需求。实现中集成主流JAX库如equinox和optax，提升用户使用熟悉度。库中包含多种基准模型，文档提供不同应用场景的参考实现及分步扩展教程。代码托管于GitLab：https://gitlab.com/mia_jinns/jinns。
原文摘要 · Abstract (English)
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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