Project Grant 2436343

This federal Project Grant award, with a total funding amount of $569,051, was provided by the National Science Foundation (NSF) under the Mathematical and Physical Sciences (CFDA 47.049) federal grant program. The award supports a collaborative research project between the University of California, Los Angeles (UCLA) and the University of Utah to develop theoretical foundations for AI-assisted digital twins to integrate scientific data, physical models, and machine learning for complex high-power laser science and engineering. The key products and services to be delivered include:

1. Extracting reduced representations of scientific data from experiments or high-fidelity simulations
2. Building data-efficient and physics-aware predictive machine learning surrogate models of laser fields with uncertainty quantification
3. Developing generative model-based rapid closed-loop control between digital models and physical high-power laser systems

This project aims to enable efficient design, failure and performance prediction, operational optimization, and exploration of emerging lasing conditions for advanced laser technologies. The award will also support the training of graduate students and postdoctoral researchers as part of developing the next generation of scientists in this field. No sub-awards are planned under this grant.