jinns: a JAX Library for Physics-Informed Neural Networks
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Abstract (arXiv)
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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References · 10
- 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,908
- 2019DeepXDE: A deep learning library for solving differential equationscited by 619
- 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
- 2022PDEBENCH: An Extensive Benchmark for Scientific Machine Learningcited by 413
- 2023PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEscited by 87
- 2023Separable Physics-Informed Neural Networkscited by 66
- 2021HyperPINN: Learning parameterized differential equations with physics-informed hypernetworkscited by 39
- 2022Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networkscited by 23
- 2021IDRLnet: A Physics-Informed Neural Network Librarycited by 10
- 2021Elvet -- a neural network-based differential equation and variational problem solvercited by 7
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