Research Paper#Physics-Informed Neural Networks (PINNs), Electromagnetics, Wave Propagation🔬 ResearchAnalyzed: Jan 3, 2026 18:53
PINNs for Electromagnetic Wave Propagation: Hybrid Training Improves Accuracy
Published:Dec 29, 2025 11:36
•1 min read
•ArXiv
Analysis
This paper addresses the challenges of using Physics-Informed Neural Networks (PINNs) for solving electromagnetic wave propagation problems. It highlights the limitations of PINNs compared to established methods like FDTD and FEM, particularly in accuracy and energy conservation. The study's significance lies in its development of hybrid training strategies to improve PINN performance, bringing them closer to FDTD-level accuracy. This is important because it demonstrates the potential of PINNs as a viable alternative to traditional methods, especially given their mesh-free nature and applicability to inverse problems.
Key Takeaways
- •PINNs are explored as an alternative to traditional methods like FDTD for electromagnetic wave propagation.
- •Hybrid training strategies are developed to address accuracy and energy conservation issues in PINNs.
- •The proposed methods include time marching, causality-aware weighting, interface continuity loss, and a local Poynting-based regularizer.
- •The results show competitive performance with FDTD in canonical electromagnetic examples, achieving high field accuracy and energy conservation.
Reference
“The study demonstrates hybrid training strategies can bring PINNs closer to FDTD-level accuracy and energy consistency.”