Advancing Fusion with Machine Learning Research Needs Workshop Report

David Humphreys,R Granetz,D Schissel,A Patra,M D Boyer,E C Cyr,C S Chang,E Lawrence,J Hittinger,V Pascucci,J Canik,A Kupresanin,E Kolemen

doi:10.1007/s10894-020-00258-1

Abstract

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.

Highlights

Dealing with all aspects associated with the generation, movement, and analysis of large sets of data (‘‘big data’’) has emerged as a critical issue for fusion and plasma science research
The Priority Research Opportunities (PRO’s) identified are: Priority Research Opportunities (PROs) 1: Science Discovery with Machine Learning includes approaches to bridge gaps in theoretical understanding through identification of missing effects using large datasets; accelerating hypothesis generation and testing; and optimizing experimental planning to help speed up progress in gaining new knowledge
The set of high-impact PROs identified in the Advancing Fusion with Machine Learning Research Needs Workshop, together with the foundational activities highlighted, will significantly accelerate and enhance research towards solving outstanding fusion problems, helping to maximize the rate of knowledge gain and progress toward a fusion power plant

Summary

Background and Motivation

The pursuit of fusion energy has required extensive experimental and theoretical science activities to develop the knowledge needed that will enable design of successful fusion power plants. The PRO’s identified are: PRO 1: Science Discovery with Machine Learning includes approaches to bridge gaps in theoretical understanding through identification of missing effects using large datasets; accelerating hypothesis generation and testing; and optimizing experimental planning to help speed up progress in gaining new knowledge. This approach to supporting and accelerating the scientific process itself has already proven to be among the most successful applications of ML/AI methods in many fields. Tasks of this PRO will include the automatic population of the Fusion Data ML Platform, with production and storage of key metadata and labels, as well as methods for rapid selection and retrieval of data to create local training and test sets

Foundational Activities and Conclusion

Introduction

Findings

Summary and Conclusions