This paper presents a novel physics-informed neural network (PINN) framework for real-time geometry optimization of Inverted E-Shaped Antennas (IESA) across multiple frequency bands. The proposed hybrid architecture combines Fourier Neural Operators (FNO) with physics-informed constraints derived from Maxwell’s equations to enable rapid prediction of optimal geometric parameters given target frequency specifications.
Key findings
Proposes a hybrid PINN-FNO architecture for parametric electromagnetic problems.
Enables gradient-based optimization of antenna geometry without adjoint simulations.
Supports multi-objective optimization for multiple bands with trade-off control.
Achieves 102–103× speedup compared to conventional finite-element method workflows.
Limitations & open questions
Real-time optimization capabilities for varying frequency specifications remain underexplored.
Multi-objective optimization for multiple frequency bands with physics-informed constraints is not adequately addressed.