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This paper presents methodological contributions for physics-informed machine learning under small-data constraints, using an abrasive waterjet milling dataset of 155 points. It shows that data curation choices, evaluation design, and physics integration form matter significantly, with Gaussian Process variants outperforming other models.
This paper proposes a Frequency Shift Physics-Informed Extreme Learning Machine (FS-PIELM) that uses an additive weight initialization mechanism to overcome spectral bias in solving high-frequency PDEs. The method achieves up to five orders of magnitude improvement over existing PIELM variants on benchmark problems.
This paper extends Port-Hamiltonian Neural Networks (PHNNs) to partial differential equations (PDEs) for learning nonlinear string dynamics from data. The approach recovers both the Hamiltonian and dissipation, outperforming non-physics-informed baselines in accuracy and interpretability.