@inproceedings{bibcite_126,
  author = {Yulong Yang and Jiayin Liu and Vineet Bansal and Christine Allen-Blanchette},
  title = {Physically Plausible Multi-System Trajectory Generation and Symmetry Discovery},
  abstract = {<p>Many recent neural network models leverage inductive biases from classical mechanics to encourage model interpretability and ensure physical consistency in dynamical forecasting. However, these models are designed to capture the dynamics of a single system with fixed physical parameters, from state-space measurements of a known configuration space. In this paper we introduce Symplectic Phase Space GAN (SPS-GAN), a conditional generative framework that learns the dynamics of multiple systems, and can generalize to unseen physical parameters. Moreover, SPS-GAN can infer the configuration space structure of the system from arbitrary measurement types including state-space measurements, and video frames. Our model embeds a Hamiltonian neural network recurrent module within a conditional GAN backbone and incorporates a physics-motivated sparsity objective to encourage discovery of structured symplectic representations. We demonstrate that SPS-GAN enables trajectory prediction, physically plausible video generation, and symmetry discovery, achieving performance competitive to supervised models designed for single systems while supporting multi-system modeling.</p>},
  year = {2026},
  journal = {Conference on Neural Information Processing Systems (NeurIPS)},
}