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arXiv · 2503.21198

Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics

Jack Hirschman·Erfan Abedi·Minyang Wang·Hao Zhang·Abhimanyu Borthakur·Justin Baker·Andrea L. Bertozzi·Randy Lemons·Sergio Carbajo
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Abstract

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.

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Jack Hirschman, Erfan Abedi, Minyang Wang, Hao Zhang, Abhimanyu Borthakur, Justin Baker, Andrea L. Bertozzi, Randy Lemons, Sergio Carbajo. 2025-11-14. Deep Learning Assisted Modeling for $χ^{(2)}$ Nonlinear Optics. https://arxiv.org/abs/2503.21198

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