Schrödinger-Type Transmission Line Equation via Physics-Informed Neural Networks (PINNs)
DOI:
https://doi.org/10.21070/jicte.v9i2.1692Keywords:
Physics-Informed Neural Networ, Schrödinger-type, split-step Fourier method, partial differential equation, transmissionAbstract
Accurate modeling of high-frequency transmission systems requires efficient methods to represent dispersive and nonlinear effects. Traditional solvers like the Split-Step Fourier Method (SSFM) are accurate but computationally demanding. The Schrödinger-Type Transmission Line Equation (STLE) effectively models these systems, yet existing techniques struggle with inverse problems and limited data. Research on data-efficient and stable frameworks for STLEs remains limited. This study introduces a Physics-Informed Neural Network (PINN) to solve forward and inverse STLE problems by embedding physical laws into the training process. The model achieved a relative L² error ≤1×10⁻³ and identified dispersion (β₂) and nonlinearity (γ) coefficients with 1–3% error using ≤5% data. Gradient-regularized losses and Fourier-enhanced inputs improve training stability and precision. The proposed approach enables reliable, data-efficient modeling of nonlinear transmission systems for advanced electromagnetic design.
Highlight :
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Shows reliable modeling of dispersive and nonlinear transmission behavior using PINNs.
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Achieves accurate coefficient identification from limited or sparse data.
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Confirms agreement with Split-Step Fourier Method results at low error levels.
Keywords : Physics-Informed Neural Network, Schrödinger-type, split-step Fourier method, partial differential equation,transmission
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Copyright (c) 2025 Zahraa Fadhil Hassan, H.K. Al-Mahdawi , Farah Hatem Khorsheed, Waqas Saad Yaseen, Walaa Badr Khudhair Alwan
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