jinns : physics-informed neural network with JAX

The jinns python package allows to solve differential equations -ODEs,  PDEs, or systems of thereof - using the framework of physics-informed neural network and the JAX ecosystem (automatic differentiation, just-in-time compilation, and more). Its interface is focused on user flexibility, allowing to easily define a "physics" loss and handling the rest. The development focuses on inverse problems, where one wishes to infer some of the differential equation parameters using observed data   Many examples notebooks are available in the package documentation : https://mia_jinns.gitlab.io/jinns/index.html

Developers & Maintainers : Hugo Gangloff, Nicolas Jouvin