Deep Learning Assisted Modeling for χ⁽²⁾ Nonlinear Optics

2025/03/27 by Jack Hirschman, Hirschman, Jack, Erfan Abedi +12
Computer Science · #Accelerator Physics (physics.acc-ph) #FOS: Physical sciences #Neural Networks and Applications #Optics (physics.optics)

paper · pdf · doi:10.48550/arxiv.2503.21198

openalex publication_date 2025/03/27 · openalex created_date 2025/10/11 · openalex updated_date 2026/07/28

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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