Unveiling Multi-regime Patterns in SciML: Distinct Failure Modes and Regime-specific Optimization
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 enforcements, and various optimizer designs; (ii) optimization effectiveness is regime-specific, with no single method performing well across all ...
| Authors | Yuxin Wang, Yuanzhe Hu, Xiaokun Zhong et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 May 2026 |
| Last updated at source | 27 May 2026 |
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