Normalized semigroup error is introduced as a diagnostic for learned simulators on 1D heat and Burgers equations; it correlates with rollout degradation (Spearman ρ=0.635) while regularization shows mixed results.
Data driven gov- erning equations approximation using deep neural networks.Journal of Computational Physics, 395: 620–635, 2019
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A product-kernel interpolation method is proposed that augments state with parameters to produce symplectic large-step predictors for Hamiltonian dynamics by construction, with error bounds that extend from the non-parameterized case.
citing papers explorer
-
Symplecticity-preserving prediction of parameter-dependent Hamiltonian dynamics by Generalized Kernel Interpolation
A product-kernel interpolation method is proposed that augments state with parameters to produce symplectic large-step predictors for Hamiltonian dynamics by construction, with error bounds that extend from the non-parameterized case.