Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics
| Autor: | Iosif B. Meyerov, Arkady Gonoskov, Erik Wallin, Alexey Polovinkin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Computer science FOS: Physical sciences lcsh:Medicine Harmonic (mathematics) Accelerator Physics and Instrumentation Machine learning computer.software_genre Article law.invention 03 medical and health sciences 0302 clinical medicine law lcsh:Science Multidisciplinary Artificial neural network business.industry lcsh:R Computational science Acceleratorfysik och instrumentering Laser-produced plasmas Computational Physics (physics.comp-ph) Laser Physics - Plasma Physics Plasma Physics (physics.plasm-ph) Identification (information) Interferometry 030104 developmental biology lcsh:Q Artificial intelligence business Physics - Computational Physics computer 030217 neurology & neurosurgery |
| Zdroj: | Scientific Reports Scientific Reports, Vol 9, Iss 1, Pp 1-15 (2019) |
| ISSN: | 2045-2322 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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