Machine learning optimizes high-power laser experiments

Commercial fusion energy plants and advanced compact radiation sources may rely on high-intensity, high-repetition rate lasers, capable of firing multiple times per second, but humans could be a limiting factor in reacting to changes at these shot rates. Applying advanced computing to this problem, a team of international scientists from LLNL, Fraunhofer Institute for Laser Technology (ILT), and the Extreme Light Infrastructure (ELI ERIC) collaborated on an experiment to optimize a high-intensity, high-repetition-rate laser using machine learning. “Our goal was to demonstrate robust diagnosis of laser-accelerated ions and electrons from solid targets at a high intensity and repetition rate,” said LLNL’s Matthew Hill, the lead researcher. “Supported by rapid feedback from a machine-learning optimization algorithm to the laser front end, it was possible to maximize the total ion yield of the system.” The researchers trained a closed-loop machine learning code developed by LLNL’s Cognitive Simulation team on laser-target interaction data to optimize the laser pulse shape, allowing it to make adjustments as the experiment ran. Data generated during the experiment was fed back into the machine learning-based optimizer, allowing it to tweak the pulse shape on the fly. Read more at LLNL News.