Advancing Fusion with Machine Learning Research Needs Workshop Report

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.

Magnetic control of tokamak plasmas through deep reinforcement learning

“…We present a new paradigm for plasma magnetic confinement on tokamaks. Our control design fulfils many of the hopes of the community for a machine-learning-based control approach 14 , including high performance, robustness to uncertain operating conditions, intuitive target specification and unprecedented versatility. This achievement required overcoming gaps in capability and infrastructure through scientific and engineering advances: an accurate, numerically robust simulator; an informed trade-off between simulation accuracy and computational complexity; a sensor and actuator model tuned to specific hardware control; realistic variation of operating conditions during training; a highly data-efficient RL algorithm that scales to high-dimensional problems; an asymmetric learning setup with an expressive critic but fast-to-evaluate policy; a process for compiling neural networks into real-time-capable code and deployment on a tokamak digital control system.…”

“…More generally, machine-learning-based approaches are being developed for magnetic-confinement control and fusion in general, not limited to control. A survey of this area is provided by Humphreys et al 14 , who categorized approaches into seven Priority Research Opportunities, including accelerating science, diagnostics, model extraction, control, large data, prediction and platform development. Early use of neural networks in a control loop for plasma control is presented by Bishop et al 15 , who used a small-scale neural network to estimate the plasma position and low-dimensional shape parameters, which were subsequently used as error signals for feedback control.…”

“…These combined benefits reduce the controller development cycle and accelerate the study of alternative plasma configurations. Indeed, artificial intelligence has recently been identified as a ‘Priority Research Opportunity’ for fusion control 14 , building on demonstrated successes in reconstructing plasma-shape parameters 15 , 16 , accelerating simulations using surrogate models 17 , 18 and detecting impending plasma disruptions 19 . RL has not, however, been used for magnetic controller design, which is challenging due to high-dimensional measurements and actuation, long time horizons, rapid instability growth rates and the need to infer the plasma shape through indirect measurements.…”

Tackling Climate Change with Machine Learning

“…Nuclear fusion reactors [577] have the potential to produce safe and carbon-free electricity using a virtually limitless hydrogen fuel supply, but currently consume more energy than they produce [146]. While considerable scientific and engineering research is still needed, ML can help accelerate this work by, e.g., guiding experimental design and monitoring physical processes; see also [360]. Fusion reactors require intelligent experimental design because they have a large number of tunable parameters; ML can help prioritize which parameter configurations should be explored during physical experiments.…”

Machine Learning for Sustainable Energy Systems

“…ML has also been used to facilitate the development of nuclear fusion technologies. Humphreys et al (144) describe previous research and future directions for the use of AI and ML in this area, including optimizing the planning of experiments, analyzing experimental results, generating data-driven models of fusion systems, detecting plasma disruptions, and contributing to reactor control. For instance, Baltz et al (145) develop a human-in-the-loop statistical method to guide the setting of experimental parameters for a magnetic confinement fusion reactor.…”