Deep learning-assisted modeling for χ(2) nonlinear optics

doi:10.1117/1.ap.8.3.036004

Deep learning-assisted modeling for χ(2) nonlinear optics

2026-05-01

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DOI：10.1117/1.ap.8.3.036004

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Modeling second-order (chi((2))) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods such as 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 a long short-term memory-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 250 & times; 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.

Keywords:

nonlinear optics

digital twin

chi ((2)) machine learning

Deep learning-assisted modeling for χ(2) nonlinear optics

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