AI & ML
PDECheck: A Diagnostic Software Suite for Auditing Learned PDE Simulators
arXiv cs.NA0 views
arXiv:2606.18200v2 Announce Type: replace-cross Abstract: PDECheck audits learned PDE simulators through callable propagators or saved predictions. It evaluates relative $L^2$ error, temporal composition, finite-difference generators, and equation-specific physical residuals, retaining horizons and missing prerequisites. Analytic and manufactured defects verify the kernels. A heat-equation sweep quantifies generator sensitivity to timestep, frequency, grid, and precision. A three-equation, five-seed FNO study compares supervised, PINO-style, and composition-regularized training. An accuracy-matched advection comparison has state errors below 0.3% and a semigroup-error ratio of 1.48 (95% interval 1.34-1.62). The composition penalty lowers semigroup error but increases composed-state and longer-rollout prediction error. A time-reparameterization check produces identical low-error endpoints with different compositions. Applications to five external equation families exercise the readers and physical contracts on coarse grids. The release includes reproducibility artifacts and measured CPU/CUDA costs.
Source:https://arxiv.org/abs/2606.18200PreviousA coupled finite element formulation for chemo-mechano-thermodynamical contact and its application to bonding and debondingNextCQRS Architecture: Separating Read and Write Models in High-Volume Systems
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