1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024
CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries
Christopher Eldred, François Gay-Balmaz, Vakhtang Putkaradze
To accurately compute data-based prediction of Hamiltonian systems, especially the long-term evolution of such systems, it is essential to utilize methods that preserve the structu…
cs.LG2023★ 1 cited
Lie-Poisson Neural Networks (LPNets): Data-Based Computing of Hamiltonian Systems with Symmetries
Christopher Eldred, François Gay-Balmaz, Sofiia Huraka +1
An accurate data-based prediction of the long-term evolution of Hamiltonian systems requires a network that preserves the appropriate structure under each time step. Every Hamilton…