Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914158041104384 |
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| author | Hirschman, Jack Abedi, Erfan Wang, Minyang Zhang, Hao Borthakur, Abhimanyu Baker, Justin Bertozzi, Andrea L. Lemons, Randy Carbajo, Sergio |
| author_facet | Hirschman, Jack Abedi, Erfan Wang, Minyang Zhang, Hao Borthakur, Abhimanyu Baker, Justin Bertozzi, Andrea L. Lemons, Randy Carbajo, Sergio |
| contents | Modeling second-order ($χ^{(2)}$) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods like the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present an LSTM-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory's Linac Coherent Light Source II. The model achieves over 250x speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_21198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics Hirschman, Jack Abedi, Erfan Wang, Minyang Zhang, Hao Borthakur, Abhimanyu Baker, Justin Bertozzi, Andrea L. Lemons, Randy Carbajo, Sergio Optics Accelerator Physics Modeling second-order ($χ^{(2)}$) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods like the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present an LSTM-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator Laboratory's Linac Coherent Light Source II. The model achieves over 250x speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks and digital twins of laser systems. |
| title | Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics |
| topic | Optics Accelerator Physics |
| url | https://arxiv.org/abs/2503.21198 |