Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics

Gonoskov, Arkady; Wallin, Erik; Половинкин, А. Н.; Meyerov, Iosif B.

doi:10.1038/s41598-019-43465-3

Cited by 34 publications

(21 citation statements)

References 69 publications

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“…Neural networks have been used by some researchers in order to identify experimental conditions [26] and even to optimise an existing plasma chemical reaction system [27], though there are no published articles at the time of writing that have applied this to plasma chemical kinetic reaction systems in order to infer which conditions might be most optimal.…”

Section: Alternatives To the Methods Described In This Workmentioning

confidence: 99%

Graph Theory Applied to Plasma Chemical Reaction Engineering

Holmes

Rothman

Zimmerman

2021

Plasma Chem Plasma Process

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This work explores the following applications of graph theory to plasma chemical reaction engineering: assembly of a weighted directional graph with the key addition of reaction nodes, from a published set of reaction data for air; graph visualisation for probing the reaction network for potentially useful or problematic reaction pathways; running Dijkstra’s algorithm between all species nodes; further analysis of the graph for useful engineering information such as which conditions, reactions, or species could be enhanced or supressed to favour particular outcomes, e.g. targeted chemical formation. The use of reaction-nodes combined with derived parameters allowed large amounts of key information regarding the plasma chemical reaction network to be assessed simultaneously using a leading open source graph visualisation software (Gephi). A connectivity matrix of Dijkstra’s algorithm between each two species gave a measure of the relative potential of species to be created and destroyed under specific conditions. Further investigation into using the graph for key reaction engineering information led to the development of a graph analysis algorithm to quantify demand for conditions for targeted chemical formation: Optimal Condition Approaching via Reaction-In-Network Analysis (OCARINA). Predictions given by running OCARINA display significant similarities to a well-known electric field strength regime for optimal ozone production in air. Time dependent 0D simulations also showed preferential formation for O· and O3 using the respective conditions generated by the algorithm. These applications of graph theory to plasma chemical reaction engineering show potential in identifying promising simulations and experiments to devote resources.

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Section: Alternatives To the Methods Described In This Workmentioning

confidence: 99%

Graph Theory Applied to Plasma Chemical Reaction Engineering

Holmes

Rothman

Zimmerman

2021

Plasma Chem Plasma Process

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“…Besides the use in simulations [26,27], numerical computations can also support experimental efforts to achieve high intensity, e.g., by controlling adaptive optics [28] or by retrieving information from the measured output based on the solution of inverse problem [29]. Numerical computations are also of clear interest for designing experiments [30][31][32][33][34][35][36][37][38][39] at the next generation large-scale laser facilities [40], where strong electromagnetic fields are likely to be reached by the combination of several, tightly focused laser pulses [41,42].…”

Section: Introductionmentioning

confidence: 99%

Optimized Computation of Tight Focusing of Short Pulses Using Mapping to Periodic Space

et al. 2021

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When a pulsed, few-cycle electromagnetic wave is focused by optics with f-number smaller than two, the frequency components it contains are focused to different regions of space, building up a complex electromagnetic field structure. Accurate numerical computation of this structure is essential for many applications such as the analysis, diagnostics, and control of high-intensity laser-matter interactions. However, straightforward use of finite-difference methods can impose unacceptably high demands on computational resources, owing to the necessity of resolving far-field and near-field zones at sufficiently high resolution to overcome numerical dispersion effects. Here, we present a procedure for fast computation of tight focusing by mapping a spherically curved far-field region to periodic space, where the field can be advanced by a dispersion-free spectral solver. In many cases of interest, the mapping reduces both run time and memory requirements by a factor of order 10, making it possible to carry out simulations on a desktop machine or a single node of a supercomputer. We provide an open-source C++ implementation with Python bindings and demonstrate its use for a desktop machine, where the routine provides the opportunity to use the resolution sufficient for handling the pulses with spectra spanning over several octaves. The described approach can facilitate the stability analysis of theoretical proposals, the studies based on statistical inferences, as well as the overall development and analysis of experiments with tightly-focused short laser pulses.

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“…Machine learning (ML) methods open novel ways for solving long-standing problems in many areas of physics, including plasma physics [1][2][3][4], condensed-matter physics [5,6], quantum physics [7][8][9][10], thermodynamics [11], quantum chemistry [12], particle physics [13] and many others [14,15]. One prominent problem for ML methods is to generalize results of numerical simulations, such that they can be used to extract unknown information from experimentally measured data.…”

Section: Introductionmentioning

confidence: 99%

Towards ML-Based Diagnostics of Laser–Plasma Interactions

Rodimkov

Bhadoria

Volokitin

et al. 2021

Sensors

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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.

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Employing machine learning for theory validation and identification of experimental conditions in laser-plasma physics

Cited by 34 publications

References 69 publications

Graph Theory Applied to Plasma Chemical Reaction Engineering

Graph Theory Applied to Plasma Chemical Reaction Engineering

Optimized Computation of Tight Focusing of Short Pulses Using Mapping to Periodic Space

Towards ML-Based Diagnostics of Laser–Plasma Interactions

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