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Jonathan Olson reacted on thishttps://lnkd.in/gErRve5J Proud to present our latest work in Science Magazine—where our team demonstrates that, through the strategic integration of high-fidelity simulations, real experiments, and machine learning, we can predict the success of nuclear fusion experiments in advance, complete with clear error bars. This is more than a milestone in fusion science. It exemplifies a new scientific paradigm: using AI to forecast outcomes of complex systems prior to running costly and time-consuming tests. Think of it as “cognitive simulation”—an AI-powered crystal ball for experimentation. Imagine how transformative this is across fields: - In biology and medicine, we could foresee treatment effects using in silico clinical trials, accelerating discovery and guiding safer experimentation. - In engineering, aerospace, climate modeling, AI-driven simulators could predict performance, safety risks, or catastrophic failure points—before building prototypes. - In domains like drug design, health diagnostics, or space systems, this approach means smarter targeting, fewer dead ends, and faster innovation. More broadly, it shapes the future of simulation intelligence—where the blending of simulation, experimentation, and AI powers predictive science. A huge shout-out to Kelli Humbird, whose leadership propelled this groundbreaking work forward, and heartfelt thanks to my brilliant coauthors for their invaluable contributions. If we can forecast fusion performance today, tomorrow we can predict and optimize across nearly any complex scientific system.Predicting fusion ignition at the National Ignition Facility with physics-informed deep learningPredicting fusion ignition at the National Ignition Facility with physics-informed deep learning
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