Uncovering the mechanisms of physics is driving a new paradigm in artificial intelligence
(AI) discovery. Today, physics has enabled us to understand the AI paradigm in a wide …

Recent advances in computational modelling of atomic systems, spanning molecules,
proteins, and materials, represent them as geometric graphs with atoms embedded as …

Lagrangian and Hamiltonian neural networks LNN and HNNs, respectively) encode strong
inductive biases that allow them to outperform other models of physical systems significantly …

Recently, graph neural networks have been gaining a lot of attention to simulate dynamical
systems due to their inductive nature leading to zero-shot generalizability. Similarly, physics …

Glass science, like other materials domains, has been advancing at a rapid pace during the
last few decades thanks to sophisticated experimental techniques, simulation methods, and …

Thermodynamics-informed neural networks employ inductive biases for the enforcement of
the first and second principles of thermodynamics. To construct these biases, a metriplectic …

Optimization of atomic structures presents a challenging problem, due to their highly rough
and non-convex energy landscape, with wide applications in the fields of drug design …

Numerous explainability methods have been proposed to shed light on the inner workings of
GNNs. Despite the inclusion of empirical evaluations in all the proposed algorithms, the …

Graph neural networks (GNNs), in general, are built on the assumption of a static set of
features characterizing each node in a graph. This assumption is often violated in practice …

This work concerns the application of physics‐informed neural networks to the modeling and
control of complex robotic systems. Achieving this goal requires extending physics‐informed …