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cs.LG2025
Reconstructing Physics-Informed Machine Learning for Traffic Flow Modeling: a Multi-Gradient Descent and Pareto Learning Approach
Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2
Physics-informed machine learning (PIML) is crucial in modern traffic flow modeling because it combines the benefits of both physics-based and data-driven approaches. In convention…
cs.LG2025
Potential failures of physics-informed machine learning in traffic flow modeling: theoretical and experimental analysis
Yuan-Zheng Lei, Yaobang Gong, Dianwei Chen +2
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…