WGFINNs use weak-form loss functions with GENERIC structure preservation to recover governing equations more accurately from noisy observations than prior strong-form GFINNs.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Stoch-IDENT identifies linear and nonlinear SPDEs from data by using sample means for the drift and a quadratic sparse regression solved via a new greedy algorithm QSP for the diffusion term, supported by identifiability analysis on mean-covariance spectra and solution-space dimensions.
citing papers explorer
-
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
WGFINNs use weak-form loss functions with GENERIC structure preservation to recover governing equations more accurately from noisy observations than prior strong-form GFINNs.
-
Stoch-IDENT: New Method and Mathematical Analysis for Identifying SPDEs from Data
Stoch-IDENT identifies linear and nonlinear SPDEs from data by using sample means for the drift and a quadratic sparse regression solved via a new greedy algorithm QSP for the diffusion term, supported by identifiability analysis on mean-covariance spectra and solution-space dimensions.