Towards ML-Based Diagnostics of Laser–Plasma Interactions

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...

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Autores principales:	Yury Rodimkov, Shikha Bhadoria, Valentin Volokitin, Evgeny Efimenko, Alexey Polovinkin, Thomas Blackburn, Mattias Marklund, Arkady Gonoskov, Iosif Meyerov
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	laser–plasma machine learning neural network dimension reduction data augmentation Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/3c3b37f45d0047e7bd7ebe672cf0bdbb
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id	oai:doaj.org-article:3c3b37f45d0047e7bd7ebe672cf0bdbb
record_format	dspace
spelling	oai:doaj.org-article:3c3b37f45d0047e7bd7ebe672cf0bdbb2021-11-11T19:01:40ZTowards ML-Based Diagnostics of Laser–Plasma Interactions10.3390/s212169821424-8220https://doaj.org/article/3c3b37f45d0047e7bd7ebe672cf0bdbb2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/6982https://doaj.org/toc/1424-8220The 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.Yury RodimkovShikha BhadoriaValentin VolokitinEvgeny EfimenkoAlexey PolovinkinThomas BlackburnMattias MarklundArkady GonoskovIosif MeyerovMDPI AGarticlelaser–plasmamachine learningneural networkdimension reductiondata augmentationChemical technologyTP1-1185ENSensors, Vol 21, Iss 6982, p 6982 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	laser–plasma machine learning neural network dimension reduction data augmentation Chemical technology TP1-1185
spellingShingle	laser–plasma machine learning neural network dimension reduction data augmentation Chemical technology TP1-1185 Yury Rodimkov Shikha Bhadoria Valentin Volokitin Evgeny Efimenko Alexey Polovinkin Thomas Blackburn Mattias Marklund Arkady Gonoskov Iosif Meyerov Towards ML-Based Diagnostics of Laser–Plasma Interactions
description	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.
format	article
author	Yury Rodimkov Shikha Bhadoria Valentin Volokitin Evgeny Efimenko Alexey Polovinkin Thomas Blackburn Mattias Marklund Arkady Gonoskov Iosif Meyerov
author_facet	Yury Rodimkov Shikha Bhadoria Valentin Volokitin Evgeny Efimenko Alexey Polovinkin Thomas Blackburn Mattias Marklund Arkady Gonoskov Iosif Meyerov
author_sort	Yury Rodimkov
title	Towards ML-Based Diagnostics of Laser–Plasma Interactions
title_short	Towards ML-Based Diagnostics of Laser–Plasma Interactions
title_full	Towards ML-Based Diagnostics of Laser–Plasma Interactions
title_fullStr	Towards ML-Based Diagnostics of Laser–Plasma Interactions
title_full_unstemmed	Towards ML-Based Diagnostics of Laser–Plasma Interactions
title_sort	towards ml-based diagnostics of laser–plasma interactions
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3c3b37f45d0047e7bd7ebe672cf0bdbb
work_keys_str_mv	AT yuryrodimkov towardsmlbaseddiagnosticsoflaserplasmainteractions AT shikhabhadoria towardsmlbaseddiagnosticsoflaserplasmainteractions AT valentinvolokitin towardsmlbaseddiagnosticsoflaserplasmainteractions AT evgenyefimenko towardsmlbaseddiagnosticsoflaserplasmainteractions AT alexeypolovinkin towardsmlbaseddiagnosticsoflaserplasmainteractions AT thomasblackburn towardsmlbaseddiagnosticsoflaserplasmainteractions AT mattiasmarklund towardsmlbaseddiagnosticsoflaserplasmainteractions AT arkadygonoskov towardsmlbaseddiagnosticsoflaserplasmainteractions AT iosifmeyerov towardsmlbaseddiagnosticsoflaserplasmainteractions
_version_	1718431638015180800

Towards ML-Based Diagnostics of Laser–Plasma Interactions

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