Preprint Article Version 1 This version is not peer-reviewed

Artificial Neural Network Trained to Predict High-Harmonic Flux

Version 1 : Received: 28 September 2018 / Approved: 28 September 2018 / Online: 28 September 2018 (11:02:36 CEST)

A peer-reviewed article of this Preprint also exists.

Gherman, A.M.M.; Kovács, K.; Cristea, M.V.; Toșa, V. Artificial Neural Network Trained to Predict High-Harmonic Flux. Appl. Sci. 2018, 8, 2106. Gherman, A.M.M.; Kovács, K.; Cristea, M.V.; Toșa, V. Artificial Neural Network Trained to Predict High-Harmonic Flux. Appl. Sci. 2018, 8, 2106.

Journal reference: Appl. Sci. 2018, 8, 2106
DOI: 10.3390/app8112106

Abstract

In this work we present the results obtained with an artificial neural network (ANN) which we trained to predict the expected output of high-order harmonic generation (HHG) process, while exploring a multi-dimensional parameter space. We argue on the utility and efficiency of the ANN model and demonstrate its ability to predict the outcome of HHG simulations. In this case study we present the results for a loose focusing HHG beamline, where the changing parameters are: the laser pulse energy, gas pressure, gas cell position relative to focus and gas cell length. The physical quantity which we predict here using ANN is directly related to the total harmonic yield in a specified spectral domain (20-40 eV). We discuss the versatility and adaptability of the presented method.

Subject Areas

high-order harmonic generation; 3D non-adiabatic model; simulation; artificial neural network; prediction

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