Modeling terahertz propagation via frequency-domain physics-informed neural networks

Terahertz time-domain spectroscopy (THz-TDS) provides a non-invasive and label-free method for probing the internal structure and electromagnetic response of materials. Numerical simulation of THz-TDS can help in understanding wave–matter interactions, guiding experimental design, and interpreting complex measurement data. However, existing simulation techniques face challenges in accurately modeling THz wave propagation with reduced computational cost during inference. Additionally, conventional simulation solvers often require dense spatial–temporal discretization, which limits their applicability to large-scale and real-time scenarios. Simplified analytical models may neglect dispersion, multiple scattering, and boundary effects. To address these limitations, we establish a novel computational framework that integrates frequency-domain physics-informed neural networks (FD-PINNs) with less data-driven ones. To validate our proposed FD-PINNs, simulation results from finite-difference time-domain and TD-PINNs were used to compare with FD-PINNs. Finally, experimental results from THz-TDS systems were employed to further exhibit accurate reconstruction ability of FD-PINNs.