用深度强化学习优化核聚变反应堆设计，提升效率并降低成本。
Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning
- 采用深度强化学习框架处理多物理约束下的设计优化
- 实现满足运行需求的最优设计，降低建造成本
- 适合关注核聚变工程与智能优化的研究者
本研究探索了深度强化学习（DRL）在核聚变反应堆设计优化中的应用。DRL 能有效应对稳态运行中多重物理与工程约束带来的挑战。研究开发了适用于并行化的聚变反应堆设计计算与优化代码。所提出的框架可找到满足运行要求且降低建造成本的最优反应堆设计。通过 DRL 实现的多目标设计优化简化了聚变反应堆的设计流程，表明该框架在推动未来高效、可持续反应堆设计方面具有高潜力。
原文摘要 · Abstract (English)
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-state operation. The fusion reactor design computation and the optimization code applicable to parallelization with DRL are developed. The proposed framework enables finding the optimal reactor design that satisfies the operational requirements while reducing building costs. Multi-objective design optimization for a fusion reactor is now simplified by DRL, indicating the high potential of the proposed framework for advancing the efficient and sustainable design of future reactors.
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