Physically-Principled Learning of Nonlinear Modal Subspaces for Real-Time Simulation

A self-supervised approach for learning physics-based nonlinear modal subspaces that directly minimizes the system's mechanical energy during training, leading to learned subspaces that reflect physical equilibrium constraints, resolve overfitting issues, and offer interpretable latent space parameters.