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Employing machine learning for theory validation and identification of experimental conditions in laserplasma physics
Umeå University, Faculty of Science and Technology, Department of Physics.
2019 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 7043Article in journal (Refereed) Published
Abstract [en]

The validation of a theory is commonly based on appealing to clearly distinguishable and describable features in properly reduced experimental data, while the use of ab-initio simulation for interpreting experimental data typically requires complete knowledge about initial conditions and parameters. We here apply the methodology of using machine learning for overcoming these natural limitations. We outline some basic universal ideas and show how we can use them to resolve long-standing theoretical and experimental difficulties in the problem of high-intensity laser-plasma interactions. In particular we show how an artificial neural network can "read" features imprinted in laser-plasma harmonic spectra that are currently analysed with spectral interferometry.

Place, publisher, year, edition, pages
Nature Publishing Group, 2019. Vol. 9, article id 7043
National Category
Accelerator Physics and Instrumentation
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URN: urn:nbn:se:umu:diva-159380DOI: 10.1038/s41598-019-43465-3ISI: 000467137300042PubMedID: 31065006OAI: oai:DiVA.org:umu-159380DiVA, id: diva2:1323548
Available from: 2019-06-12 Created: 2019-06-12 Last updated: 2019-06-12Bibliographically approved

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Wallin, Erik

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf