Learning symplectic model reduction based on a approximation theorem of symplectic embeddings
The episode discusses a paper presenting SpAE, an autoencoder designed to perform model reduction on complex Hamiltonian systems. The authors prove that by building the network based on approximation theorems for symplectic embeddings, they can achieve accurate reconstruction and stable long-term prediction. This method significantly outperforms standard linear approaches in simulations of physical systems.