Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics
Artikel i vetenskaplig tidskrift, 2019

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.


Arkady Gonoskov

Chalmers, Fysik, Teoretisk fysik

Russian Academy of Sciences

Lobachevsky University

Göteborgs universitet

Erik Karl Wallstén Wallin

Umeå universitet

A. Polovinkin

Intel Corporation

I. Meyerov

Lobachevsky University

Scientific Reports

2045-2322 (ISSN)

Vol. 9 1 7043


Atom- och molekylfysik och optik

Annan fysik

Bioinformatik (beräkningsbiologi)



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