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The paper presents a controlled matched-integrator evaluation of Hamiltonian Neural Networks against a parameter-matched baseline on pendulum and Kepler dynamics, showing significant reductions in energy drift and trajectory error. It also examines the behavior of Störmer–Verlet-style rollouts with learned non-separable Hamiltonians.
A systematic literature review of 212 studies investigates how Physics-Informed Machine Learning (PIML) is applied in Prognostics and Health Management (PHM), introducing a four-class classification scheme and finding that PIML consistently improves predictive performance over conventional baselines, though the literature is skewed toward batteries and bearings and lacks strong evidence for claims regarding generalization and interpretability.
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.