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[2602.13811] A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization
Summary
This article presents a Physics-Informed Neural Network (PINN) model for simulating coupled electro- and elastodynamic wave propagation, achieving notable accuracy in displacement and electric potential predictions.
Why It Matters
The integration of physical laws into neural networks represents a significant advancement in scientific machine learning (SciML). This research demonstrates the potential of PINNs in solving complex, time-dependent PDE systems, which is crucial for applications in engineering and physics.
Key Takeaways
- PINNs effectively model coupled electro-elastodynamic systems.
- The proposed model achieved global relative L2 errors of 2.34% and 4.87% for displacement and electric potential, respectively.
- Challenges remain in error accumulation and stiffness in coupled eigenvalue systems.
Computer Science > Neural and Evolutionary Computing arXiv:2602.13811 (cs) [Submitted on 14 Feb 2026] Title:A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization Authors:Suhas Suresh Bharadwaj, Reuben Thomas Thovelil View a PDF of the paper titled A Unified Physics-Informed Neural Network for Modeling Coupled Electro- and Elastodynamic Wave Propagation Using Three-Stage Loss Optimization, by Suhas Suresh Bharadwaj and Reuben Thomas Thovelil View PDF HTML (experimental) 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. This work studies the application of PINNs to solve a one dimensional coupled electro-elastodynamic system modeling linear piezoelectricity in stress-charge form, governed by elastodynamic and electrodynamic equations. Our simulation employs a feedforward architecture, mapping space-time coordinates to mechanical displacement and electric potential. Our PINN model achieved global relative L2 errors of 2.34 and 4.87 percent for displacement and electric potential respectively. The results validate PINNs as effective mesh free solvers for coupled time-dependent PDE systems, though challenges remain regarding error accumulation and stiffness in coupled eigenvalue systems. Comments: Subjects: Neural and Evolu...