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News/Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
arxiv
PublishedJuly 22, 2026 at 4:00 AM

Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis

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arXiv:2505.11491v4 Announce Type: replace Abstract: This study investigates why physics-informed machine learning (PIML) can fail in macroscopic traffic flow modeling. We define failure as cases where a PIML model underperforms both purely data-driven and purely physics-based baselines by a given th

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