THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918155011489792 |
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| author | Zhu, Pengfei Maldague, Xavier |
| author_facet | Zhu, Pengfei Maldague, Xavier |
| contents | Terahertz time-domain spectroscopy (THz-TDS) is a powerful tool for extracting optical and electrical parameters, as well as probing internal structures and surface morphology of materials. However, conventional simulation techniques, such as the finite element method (FEM) and finite-difference time-domain (FDTD), face challenges in accurately modeling THz wave propagation. These difficulties arise from the broad operating bandwidth of THz-TDS (~0.1-10 THz), which demands extremely high temporal resolution to capture pulse dynamics and fine spatial resolution to resolve microstructures and interface effects. To address these limitations, we introduce physics-informed neural networks (PINNs) into THz-TDS modeling for the first time. Through a comprehensive analysis of forward problems in the time domain, we demonstrate the feasibility and potential of PINNs as a powerful framework for advancing THz wave simulation and analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_07161 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks Zhu, Pengfei Maldague, Xavier Optics Terahertz time-domain spectroscopy (THz-TDS) is a powerful tool for extracting optical and electrical parameters, as well as probing internal structures and surface morphology of materials. However, conventional simulation techniques, such as the finite element method (FEM) and finite-difference time-domain (FDTD), face challenges in accurately modeling THz wave propagation. These difficulties arise from the broad operating bandwidth of THz-TDS (~0.1-10 THz), which demands extremely high temporal resolution to capture pulse dynamics and fine spatial resolution to resolve microstructures and interface effects. To address these limitations, we introduce physics-informed neural networks (PINNs) into THz-TDS modeling for the first time. Through a comprehensive analysis of forward problems in the time domain, we demonstrate the feasibility and potential of PINNs as a powerful framework for advancing THz wave simulation and analysis. |
| title | THz-PINNs: Time-Domain Forward Modeling of Terahertz Spectroscopy with Physics-Informed Neural Networks |
| topic | Optics |
| url | https://arxiv.org/abs/2509.07161 |