Summary of Jinns: a Jax Library For Physics-informed Neural Networks, by Hugo Gangloff et al.
jinns: a JAX Library for Physics-Informed Neural Networks
by Hugo Gangloff, Nicolas Jouvin
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The jinns library is an open-source Python tool for physics-informed neural networks that can handle both forward and inverse problems, as well as meta-model learning. Built within the JAX ecosystem, it provides a flexible framework for efficiently prototyping real-world applications while allowing easy extensions to specific needs. The implementation leverages popular JAX libraries like equinox and optax for model definition and optimization, making it familiar to users. Many models are available as baselines, along with reference implementations of different use cases and step-by-step tutorials for customizations. Jinns’ code is available on GitLab. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Jinns is a special kind of computer program that helps scientists solve problems by using artificial intelligence and the laws of physics together. It’s designed to work fast and be easy to customize, which makes it useful for many different types of projects. The people who created jinns wanted to make it simple to use, so they used other popular programs as building blocks. Jinns has a lot of pre-made examples and tutorials that can help you get started with your own project. |
Keywords
» Artificial intelligence » Optimization
