  Bridging physics and deep learning is a topical challenge. While deep
learning frameworks open avenues in physical science, the design of
physically-consistent deep neural network architectures is an open issue. In
the spirit of physics-informed NNs, PDE-NetGen package provides new means to
automatically translate physical equations, given as PDEs, into neural network
architectures. PDE-NetGen combines symbolic calculus and a neural network
generator. The later exploits NN-based implementations of PDE solvers using
Keras. With some knowledge of a problem, PDE-NetGen is a plug-and-play tool to
generate physics-informed NN architectures. They provide
computationally-efficient yet compact representations to address a variety of
issues, including among others adjoint derivation, model calibration,
forecasting, data assimilation as well as uncertainty quantification. As an
illustration, the workflow is first presented for the 2D diffusion equation,
then applied to the data-driven and physics-informed identification of
uncertainty dynamics for the Burgers equation.
