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cs.CE2026
Probabilistic Physics-Informed Neural Solvers for Woods-Saxon Parameter Identification: A Coupled Forward-Inverse Approach
Iraklis Spyrou, Christos Tsepas, Vaia Prassa +1
The Woods-Saxon mean-field approach offers a compact description of bound single-particle motion in finite nuclei, while physics-informed neural networks (PINNs) provide a differen…
cs.CE2026
An Explainable Physics-Informed Neural Frequency-Response Framework for Shunt-Parameter Identification in Semi-Active Piezoelectric Tuned Mass Dampers
Andreas Georgiou, Vasileios Gkatsis, Vasileios Sioros +3
This paper proposes a Physics-Informed Neural Frequency Response Framework for learning and interpreting the frequency-domain behavior of semi-active shunted piezoelectric tuned ma…
cs.CE2026
Towards trajectory-unsupervised physics-informed neural solvers for molecular dynamics
Petros Triantafyllos, Panagiotis Krokidas, Christoforos Rekatsinas
Molecular dynamics (MD) simulations are governed by explicit equations of motion, yet most neural approaches that accelerate or emulate MD rely on simulator-generated trajectories,…