Thermodynamics-informed graph neural networks

Understand

In this paper we present a deep learning method to predict the temporal evolution of dissipative dynamic systems.

• We propose using both geometric and thermodynamic inductive biases to improve accuracy and generalization of the resulting integration scheme.
• The first is achieved with Graph Neural Networks, which induces a non-Euclidean geometrical prior with permutation invariant node and edge update functions.
• The second bias is forced by learning the GENERIC structure of the problem, an extension of the Hamiltonian formalism, to model more general non-conservative dynamics.