Lie-Poisson neural networks (LPNets): data-based computing of Hamiltonian systems with symmetries
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 Hamiltonian system contains two essential ingredients: the Poisson bracket and the Hamiltonian. Hamiltonian systems with symmetr...
Main Authors: | Eldred, Christopher, Gay-Balmaz, François, Huraka, Sofiia, Putkaradze, Vakhtang |
---|---|
Other Authors: | School of Physical and Mathematical Sciences |
Format: | Journal Article |
Language: | English |
Published: |
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/180076 |
Similar Items
-
A new canonical affine bracket formulation of Hamiltonian classical field theories of first order
by: Gay-Balmaz, François, et al.
Published: (2024) -
On the Hamiltonian and geometric structure of Langmuir circulation
by: Yang, Cheng
Published: (2023) -
Lie symmetries of nonlinear systems with unknown inputs
by: Shi, Xiaodong, et al.
Published: (2023) -
A hierarchical matrix adaptation on a family of iterative method for solving poisson equation
by: Nik Mazlan, Nik Amir Syafiq
Published: (2016) -
General relativistic Lagrangian continuum theories part I: reduced variational principles and junction conditions for hydrodynamics and elasticity
by: Gay-Balmaz, François
Published: (2024)