Data and code for Scheduled Momentum Diffusion Improves Hydraulic Shock Fidelity in Strong-Form Shallow-Water Physics-Informed Neural Networks

<pre>Companion data and code for "Scheduled Momentum Diffusion Improves Hydraulic Shock Fidelity in Strong-Form Shallow-Water Physics-Informed Neural Networks" by Huicong An and Chaojun Ouyang.<br><br>The package includes 102 formal shallow-water PINN experiments covering nine dam-break depth ratios (2:1 to 10:1), diffusion ablations, bounded Fourier/RAR/weak-form comparisons, a strong learned bed step, radial two-dimensional flow, a matched conservative-residual comparison, alternative diffusion operators, and steady subcritical flow over a smooth bump.

Formal comparisons retain all three seeds (2025, 2026 and 2027).<br><br>Contents comprise source code, resolved physical/training configurations, prediction fields, per-seed metrics, analytical references, three radial FVM reference grids, four Xia2017 smooth-step cross-checks, figure/table source data, and executable reproduction commands. The model archive contains 102 final states and 48 additional states supporting the training-evolution analysis.