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.
@article{arxiv.2503.21198,
title = {Deep Learning Assisted Modeling for $\chi^{(2)}$ Nonlinear Optics},
author = {Jack Hirschman and Erfan Abedi and Minyang Wang and Hao Zhang and Abhimanyu Borthakur and Justin Baker and Andrea L. Bertozzi and Randy Lemons and Sergio Carbajo},
journal= {arXiv preprint arXiv:2503.21198},
year = {2025}
}