arXiv Machine Learning July 21, 2026 By Suhas Suresh Bharadwaj, Reuben Thomas Thovelil

A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization

Read the original on arXiv Machine Learning →

arXiv:2602. 13811v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks present a novel approach in SciML that integrates physical laws in the form of partial differential equations directly into the NN through soft constraints in the loss function.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems

Physics-informed neural networks (PINNs) provide a promising framework for solving partial differential equations while embedding the underlying physical laws directly into the learning process. This study presents a PINN-based framework for modeling transient elastodynamic wave propagation in bimaterial systems governed by the axisymmetric equations of linear elasticity.

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A Physics-Informed Neural Network Framework for Elastodynamic Wave Propagation in Bimaterial Systems

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