Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

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推进融合与机器学习研究需求研讨会报告

近年来，机器学习和人工智能 (ML/AI) 方法已成功用于解决许多领域的问题，包括图像识别、无监督和监督分类、游戏、系统识别和预测以及自动驾驶车辆控制。数据驱动的机器学习方法也已应用于聚变能研究 20 多年，包括在中断预测、替代模型生成和实验规划领域取得的重大进展。专门用于大规模并行计算的强大专用计算机的出现，以及统计推理算法的进步，大大增强了这些计算方法提取科学知识和弥合理论模型与实际实现之间差距的能力。近年来各种 ML/AI 应用的大规模商业成功，包括机器人技术、工业流程、在线图像识别、金融系统预测和自动驾驶汽车，进一步证明了数据驱动方法在许多领域产生巨大变革的潜力. 这些进步，以及弥合 ITER 等反应堆设计和运行知识关键差距的紧迫性，推动了美国政府和世界各地计划扩大 ML/AI 的努力。能源部 (DOE) 聚变能源科学 (FES) 和高级科学计算研究 (ASCR) 科学计划办公室组织了多项活动，以确定将 ML/AI 方法应用于聚变能研究的最佳策略和方法。本文描述了 2019 年 4 月 30 日至 5 月 2 日在马里兰州盖瑟斯堡举行的由 FES/ASCR DOE 赞助的推进机器学习融合研究需求研讨会的结果（完整报告可在 https://science.osti. gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf）。研讨会吸引了 FES 和 ASCR 科学界的广泛代表，并确定了七个在推进聚变能方面具有巨大潜力的优先研究机会 (PRO)。除了 PRO 主题本身之外，研讨会还确定了最大限度地提高 ML/AI 方法在聚变能源科学中的有效性的研究指南，其中包括关注不确定性量化、量化模型和算法有效性区域的方法以及应用高度集成的团队ML/AI 数学家，