Achievement of Sustained Net Plasma Heating in a Fusion Experiment with the Optometrist Algorithm

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Received: 17 January 2017 Accepted: 14 June 2017 Published online: 25 July 2017
Achievement of Sustained Net Plasma Heating in a Fusion Experiment with the Optometrist Algorithm
E. A. Baltz1, E. Trask2, M. Binderbauer2, M. Dikovsky1, H. Gota2, R. Mendoza2, J. C. Platt P. F. Riley1
Many elds of basic and applied science require e ciently exploring complex systems with high
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dimensionality. An example of such a challenge is optimising the performance of plasma fusion experiments. The highly-nonlinear and temporally-varying interaction between the plasma, its environment and external controls presents a considerable complexity in these experiments. A further di culty arises from the fact that there is no single objective metric that fully captures both plasma quality and equipment constraints. To e ciently optimise the system, we develop the Optometrist Algorithm, a stochastic perturbation method combined with human choice. Analogous to getting an eyeglass prescription, the Optometrist Algorithm confronts a human operator with two alternative experimental settings and associated outcomes. A human operator then chooses which experiment produces subjectively better results. This innovative technique led to the discovery of an unexpected record con nement regime with positive net heating power in a eld-reversed con guration plasma, characterised by a >50% reduction in the energy loss rate and concomitant increase in ion temperature and total plasma energy.
Complex systems with many parameters are common in biology, physics, engineering, geology, and social sci- ence. Understanding and optimising such systems is di cult due to time-dependent parameters combined with noisy and missing measurements. Research progress in these disciplines can be accelerated with new tools capable of e cient exploration.
Modern plasma fusion research is a good example of this kind of complex system. Typical fusion systems have many control and input parameters, such as voltages applied to magnets, electrodes, limiters, etc. To complicate matters, the equipment has uncontrolled dri : for example, varying conditions of the internal surfaces of the vac- uum vessel may strongly a ect an experimental run. Optimising plasma requires optimisation over many highly nonlinear and coupled parameters. Further, each experiment may take a long time. ese factors preclude an e ective mapping of the high-dimensional parametric space by typical one-variable-at-a-time methods1.
Two additional complications arise because plasma fusion apparatuses are experimental and one-of-a-kind. First, the goodness metric for plasma is not fully established and objective: some amount of human judgement is required to assess an experiment. Second, the boundaries of safe operation are not fully understood: it would be easy for a fully-automated optimisation algorithm to propose settings that would damage the apparatus and set back progress by weeks or months.
To increase the speed of learning and optimisation of plasma, we developed the Optometrist Algorithm. Just as in a visit to an optometrist, the algorithm o ers a pair of choices to a human, and asks which one is preferable. Given the choice, the algorithm proceeds to o er another choice. While an optometrist asks a patient to choose between lens prescriptions based on clarity, our algorithm asks a human expert to choose between plasma settings based on experimental outcomes. e Optometrist Algorithm attempts to optimise a hidden utility model that the human experts may not be able to express explicitly.
1Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA, 94043, USA. 2Tri Alpha Energy Inc., P.O. Box 7010, Rancho Santa Margarita, CA, 92688, USA. Correspondence and requests for materials should be addressed to E.A.B. (email: eabaltz@google.com)
Scientific REPORTS | 7: 6425 | DOI:10.1038/s41598-017-06645-7 1