Develops associative truncated products on polynomial spaces to construct hyperbolicity-preserving stochastic Galerkin discretizations for conservation laws, with consistency results and applications to Euler equations.
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Symplectic neural networks enable efficient training of Hamiltonian models with implicit integrators for improved energy conservation in chaotic systems.
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Symplectic Neural Networks for learning Generalized Hamiltonians
Symplectic neural networks enable efficient training of Hamiltonian models with implicit integrators for improved energy conservation in chaotic systems.