Learning with PDE conservation laws
October 15, 2019 — June 3, 2024
Unlike PINNs which penalize deviation from conservation laws in the loss, we can impose symmetries in our neural net architecture itself.
TBD
1 References
Bloem-Reddy, and Teh. 2020. “Probabilistic Symmetries and Invariant Neural Networks.”
Di Giovanni, Rowbottom, Chamberlain, et al. 2022. “Graph Neural Networks as Gradient Flows.”
Mialon, Garrido, Lawrence, et al. 2024. “Self-Supervised Learning with Lie Symmetries for Partial Differential Equations.”
Pestourie, Mroueh, Rackauckas, et al. 2022. “Physics-Enhanced Deep Surrogates for PDEs.”
Rezende, Racanière, Higgins, et al. 2019. “Equivariant Hamiltonian Flows.” In Machine Learning and the Physical Sciences Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS).
Ruhe, Gupta, de Keninck, et al. 2023. “Geometric Clifford Algebra Networks.” In arXiv Preprint arXiv:2302.06594.
Smets, Portegies, Bekkers, et al. 2023. “PDE-Based Group Equivariant Convolutional Neural Networks.” Journal of Mathematical Imaging and Vision.