Researchers at Stanford University, UCLA, and SLAC National Accelerator Laboratory have created an artificial intelligence system that substantially speeds up simulations of light behavior in laser systems. The advancement applies deep learning techniques to model second-order nonlinear optics, which involves energy exchange between light waves traveling through specially designed crystals. This capability is crucial for facilities like SLAC’s upgraded Linac Coherent Light Source, where laser pulses must undergo precise transformations to generate intense X-rays used in scientific research.
Conventional laser simulations rely on solving the nonlinear Schrödinger equation using a technique called the split-step Fourier method. While this approach produces accurate results, it demands substantial computational resources, accounting for approximately 95 percent of processing time in complete laser system simulations. The traditional method repeatedly converts calculations between time and frequency representations, creating a significant computational bottleneck.
The research team addressed this limitation by developing a neural network model based on LSTM architecture, adapting earlier work that predicted laser pulse behavior in optical fibers to handle more complex interactions involving multiple light waves. Rather than switching between time and frequency calculations, the AI model operates using a compressed frequency representation, eliminating repetitive computations inherent in traditional methods. Testing demonstrated that the system accurately reproduced laser pulse behavior across diverse conditions, including pulses with frequency gaps and significant optical phase variations.
When processed using graphics processing units, the AI model reduced calculation time to milliseconds, representing a dramatic improvement in speed without substantially compromising accuracy. The system also captured the interconnected behavior of interacting light waves, suggesting it learned fundamental features of the interactions rather than simply reproducing isolated outputs. Researchers envision integrating these AI models directly into operating laser systems through a modular design approach, potentially enabling digital twins that mirror equipment behavior and supporting real-time adaptive control and adjustment during experiments.
The work was published in the journal Advanced Photonics and suggests applications extending beyond faster simulations to include enhanced diagnostic capabilities and more responsive laser system control across scientific facilities worldwide.
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