The data-driven future of high-energy-density physics

Peter W. Hatfield (), Jim A. Gaffney (), Gemma J. Anderson (), Suzanne Ali, Luca Antonelli, Suzan Başeğmez du Pree, Jonathan Citrin, Marta Fajardo, Patrick Knapp, Brendan Kettle, Bogdan Kustowski, Michael J. MacDonald, Derek Mariscal, Madison E. Martin, Taisuke Nagayama, Charlotte A. J. Palmer, J. Luc Peterson, Steven Rose, J J Ruby, Carl Shneider, Matt J. V. Streeter, Will Trickey and Ben Williams
Additional contact information
Peter W. Hatfield: University of Oxford
Jim A. Gaffney: Lawrence Livermore National Laboratory
Gemma J. Anderson: Lawrence Livermore National Laboratory
Suzanne Ali: Lawrence Livermore National Laboratory
Luca Antonelli: University of York
Suzan Başeğmez du Pree: Nikhef, National Institute for Subatomic Physics
Jonathan Citrin: DIFFER—Dutch Institute for Fundamental Energy Research
Marta Fajardo: Instituto Superior Técnico
Patrick Knapp: Sandia National Laboratories
Brendan Kettle: Imperial College London
Bogdan Kustowski: Lawrence Livermore National Laboratory
Michael J. MacDonald: Lawrence Livermore National Laboratory
Derek Mariscal: Lawrence Livermore National Laboratory
Madison E. Martin: Lawrence Livermore National Laboratory
Taisuke Nagayama: Sandia National Laboratories
Charlotte A. J. Palmer: Queen’s University Belfast
J. Luc Peterson: Lawrence Livermore National Laboratory
Steven Rose: University of Oxford
J J Ruby: University of Rochester
Carl Shneider: Dutch National Center for Mathematics and Computer Science (CWI)
Matt J. V. Streeter: Imperial College London
Will Trickey: University of York
Ben Williams: AWE Plc, Aldermaston

Nature, 2021, vol. 593, issue 7859, 351-361

Abstract: Abstract High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. From a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

Date: 2021
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DOI: 10.1038/s41586-021-03382-w

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