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

Journal Article · · Nature (London)
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  1. Univ. of Oxford (United Kingdom). Clarendon Lab.
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Univ. of York (United Kingdom). York Plasma Inst.
  4. National Institute for Subatomic Physics (Nikhef), Amsterdam (Netherlands)
  5. Dutch Institute for Fundamental Energy Research (DIFFER), Eindhoven (Netherlands)
  6. Inst. of Superior Tecnico (IST), Lisbon (Portugal). Inst. de Plasmas e Fusão Nuclear
  7. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  8. Imperial College London (United Kingdom)
  9. Queen's Univ. Belfast (United Kingdom)
  10. Univ. of Oxford (United Kingdom). Clarendon Lab.; Imperial College London (United Kingdom)
  11. Univ. of Rochester, NY (United States). Lab. for Laser Energetics
  12. Dutch National Center for Mathematics and Computer Science (CWI), Amsterdam (Netherlands)
  13. AWE Plc, Aldermaston (United Kingdom)
+ Show Author Affiliations
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. Furthermore, 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.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE National Nuclear Security Administration (NNSA), Office of Defense Science
Grant/Contract Number:
AC52-07NA27344; NA0003525
OSTI ID:
1831171
Alternate ID(s):
OSTI ID: 1840119
Report Number(s):
LLNL-JRNL--811857; SAND--2021-2663J; 694585
Journal Information:
Nature (London), Journal Name: Nature (London) Journal Issue: 7859 Vol. 593; ISSN 0028-0836
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

70 PLASMA PHYSICS AND FUSION TECHNOLOGY
Laboratory astrophysics
Laser-produced plasmas
Nuclear fusion and fission
Scientific data