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Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning

Autor: Kim, Jinsu, Seo, Jaemin

This research explores the application of Deep Reinforcement Learning (DRL) to optimize the design of a nuclear fusion reactor. DRL can efficiently address the challenging issues attributed to multiple physics and engineering constraints for steady-s

Externí odkaz: http://arxiv.org/abs/2409.08231

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Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas

Autor: Jalalvand, Azarakhsh, Kim, SangKyeun, Seo, Jaemin, Hu, Qiming, Curie, Max, Steiner, Peter, Nelson, Andrew Oakleigh, Na, Yong-Su, Kolemen, Egemen

A non-linear system governed by multi-spatial and multi-temporal physics scales cannot be fully understood with a single diagnostic, as each provides only a partial view, leading to information loss. Combining multiple diagnostics may also result in

Externí odkaz: http://arxiv.org/abs/2405.05908

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Enhancing Disruption Prediction through Bayesian Neural Network in KSTAR

Autor: Kim, Jinsu, Lee, Jeongwon, Seo, Jaemin, Ghim, Young-Chul, Lee, Yeongsun, Na, Yong-Su

Disruption in tokamak plasmas, stemming from various instabilities, poses a critical challenge, resulting in detrimental effects on the associated devices. Consequently, the proactive prediction of disruptions to maintain stability emerges as a param

Externí odkaz: http://arxiv.org/abs/2312.12979

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