Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization

arXiv:2605.29153v1 Announce Type: new Abstract: Neural networks trained under different hyperparameter settings can fall into distinct training "regimes," with consistent behavior within regimes and qualitative differences across regimes. In this paper, we study such multi-regime behavior in scientific machine learning (SciML) models through a regime-aware diagnostic framework that jointly analyzes performance, training dynamics, and loss-landscape geometry. We identify three key findings: (i) a consistent three-regime structure emerges across many standard SciML models, different constraint e

Source: arXiv cs.LG — read the full report at the original publisher.

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