A physics-informed graph neural network conserving linear and angular momentum for dynamical systems.

ABSTRACT: Accurate, interpretable, and real-time modeling of multi-body dynamical systems is essential for predicting behaviors and inferring physical properties in natural and engineered environments. Traditional physics-based models face scalability challenges and are computationally demanding, while data-driven approaches like Graph neural networks (GNNs) often lack physical consistency, interpretability, and generalization. In this paper, we propose DYNAMI-CAL GRAPHNET, a Physics-Informed Graph Neural Network that integrates the learning capabilities of GNNs with physics-based inductive biases to address these limitations. DYNAMI-CAL GRAPHNET enforces pairwise conservation of linear and angular momentum for interacting nodes using edge-local reference frames that are equivariant to rotational sy

SUBMITTER: Sharma V 

PROVIDER: S-EPMC12848086 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature