Avoiding Fusion Plasma Tearing Instability with Deep Reinforcement Learning скачать в хорошем качестве

Avoiding Fusion Plasma Tearing Instability with Deep Reinforcement Learning

Talk given by Dr. Egemen Kolemen during the 2024-03-28 colloquia co-hosted by the Institute of Natural Sciences of SJTU, and the School of Nuclear Science and Technology of USTC. Uploaded with the permission of Dr. Egemen Kolemen. Speaker: Egemen Kolemen, Princeton University Time: 2024-03-28 09:00 ~ 10:00 (Asia/Shanghai Time) Venue: Online Meeting Abstract: Fusion promises to be the ultimate green energy source of the future, as it is abundant, clean, and greenhouse-emission free, without the intermittency and location restrictions of solar and wind energy or fission’s safety and waste issues. While our current knowledge of plasma physics and technical capabilities is sufficiently mature for us to attempt to build fusion power reactors, the path to economic competitiveness lies with compact, high-energy-density fusion reactors. This requires operation at, simultaneously, physics parameters that are close to the edge of plasma instabilities and the technical possibilities of materials engineering and nuclear operation, which is challenging. Artificial Intelligence and Machine Learning (AI/ML) help us tackle some of these fusion challenges. I will talk about some of our recent accomplishments in application of AI/ML to fusion reactors: 1) Robust plasma state prediction even when there is sensor failure 2) Finding the minimal set of diagnostics needed to operate a reactor 3) Fusing data from multiple diagnostics to obtain new physics insights 4) Prediction of plasma evolution by combining experimental data and simulations 5) Reinforcement learning control that achieves high performance fusion reactor operation without instabilities. Bio: Egemen Kolemen is an Associate Professor at Princeton University's Mechanical & Aerospace Engineering jointly appointed with the Andlinger Center for Energy and the Environment and the Princeton Plasma Physics Laboratory (PPPL). He is the director of the Program in Sustainable Energy, recipient of the David J. Rose Excellence in Fusion Engineering Award and an ITER Scientist Fellow. His research combines engineering and physics analysis to enable economically feasible fusion reactors. He currently leads research on machine learning, real-time diagnostics and control at KSTAR, NSTX-U and DIII-D. He directs liquid metal divertor and low temperature diagnostics labs. On the theoretical side, his group develops software for stellarator optimization and economical analysis of fusion reactor.