PE-MHL incrementally refines a physics baseline with modular sub-models, proving monotonic non-increasing training error that converges, and outperforming monolithic networks on NARX and Quanser Aero benchmarks.
Robustness of physics-informed neural networks to noise in sensor data,
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PE-MHL: Physics-Encoded Modular Hybrid Layers for Scalable Learning of Complex Systems
PE-MHL incrementally refines a physics baseline with modular sub-models, proving monotonic non-increasing training error that converges, and outperforming monolithic networks on NARX and Quanser Aero benchmarks.