Publications

Learning Relativistic Geodesics and Chaotic Dynamics via Stabilized Lagrangian Neural Networks

We propose Hessian regularization and physics-aware improvements to address training instabilities in Lagrangian Neural Networks. Our methods enable learning complex chaotic systems like triple pendulums and extracting spacetime metrics from geodesic trajectories in general relativistic settings, including AdS₄ spacetime.