Checkout jinns v1.7.0

Changelog: we introduced cool things in this new version, like the solve_alternate() function to do coordinate descent on the loss in inverse problem. It allows fine-grained control over optimisation with respect to each parameters (e.g. constrained optimization, regularization, etc.).

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jinns is a Python package for physics-informed neural networks (PINNs) we develop together with Hugo Gangloff as a basis for our research. Using the JAX ecosystem, it provides an intuitive and flexible interface for

• forward problem: learning a PDE solution.
• inverse problem: learning the parameters of a PDE.
• meta-modeling: learning a family of PDE indexed by its parameters.

Check out the documentation: https://mia_jinns.gitlab.io/jinns/

Want to use or contribute ? Development happens on Gitlab