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Towards ML-Based Diagnostics of Laser–Plasma Interactions

Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia
Department of Physics, University of Gothenburg, SE-41296 Gothenburg, Sweden
Mathematical Center, Lobachevsky University, 603950 Nizhni Novgorod, Russia
Institute of Applied Physics of the Russian Academy of Sciences, 603950 Nizhni Novgorod, Russia
Adv Learning Systems, TDATA, Intel, Chandler, AZ 85226, USA
Authors to whom correspondence should be addressed.
Academic Editors: Danil Prokhorov and Alexander N. Gorban
Sensors 2021, 21(21), 6982;
Received: 4 September 2021 / Revised: 7 October 2021 / Accepted: 19 October 2021 / Published: 21 October 2021
(This article belongs to the Special Issue Robust and Explainable Neural Intelligence)
The power of machine learning (ML) in feature identification can be harnessed for determining quantities in experiments that are difficult to measure directly. However, if an ML model is trained on simulated data, rather than experimental results, the differences between the two can pose an obstacle to reliable data extraction. Here we report on the development of ML-based diagnostics for experiments on high-intensity laser–matter interactions. With the intention to accentuate robust, physics-governed features, the presence of which is tolerant to such differences, we test the application of principal component analysis, data augmentation and training with data that has superimposed noise of gradually increasing amplitude. Using synthetic data of simulated experiments, we identify that the approach based on the noise of increasing amplitude yields the most accurate ML models and thus is likely to be useful in similar projects on ML-based diagnostics. View Full-Text
Keywords: laser–plasma; machine learning; neural network; dimension reduction; data augmentation laser–plasma; machine learning; neural network; dimension reduction; data augmentation
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MDPI and ACS Style

Rodimkov, Y.; Bhadoria, S.; Volokitin, V.; Efimenko, E.; Polovinkin, A.; Blackburn, T.; Marklund, M.; Gonoskov, A.; Meyerov, I. Towards ML-Based Diagnostics of Laser–Plasma Interactions. Sensors 2021, 21, 6982.

AMA Style

Rodimkov Y, Bhadoria S, Volokitin V, Efimenko E, Polovinkin A, Blackburn T, Marklund M, Gonoskov A, Meyerov I. Towards ML-Based Diagnostics of Laser–Plasma Interactions. Sensors. 2021; 21(21):6982.

Chicago/Turabian Style

Rodimkov, Yury, Shikha Bhadoria, Valentin Volokitin, Evgeny Efimenko, Alexey Polovinkin, Thomas Blackburn, Mattias Marklund, Arkady Gonoskov, and Iosif Meyerov. 2021. "Towards ML-Based Diagnostics of Laser–Plasma Interactions" Sensors 21, no. 21: 6982.

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