Next Article in Journal
Adhesion Behavior between Multilayer Graphene and Semiconductor Substrates
Next Article in Special Issue
Laser-Control of Ultrafast π-Electron Ring Currents in Aromatic Molecules: Roles of Molecular Symmetry and Light Polarization
Previous Article in Journal
The Influence of Crack Modes on the Elastic Wave Propagation Characteristics in a Non-Uniform Rotating Shaft
Previous Article in Special Issue
Ab Initio Simulation of Attosecond Transient Absorption Spectroscopy in Two-Dimensional Materials
Open AccessArticle

Artificial Neural Network Trained to Predict High-Harmonic Flux

1
National Institute for R&D of Isotopic and Molecular Technologies, Donat str. 67-103, 400293 Cluj-Napoca, Romania
2
Chemical Engineering Department, Babeș-Bolyai University, Arany János str. 11, 400028 Cluj-Napoca, Romania
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2106; https://doi.org/10.3390/app8112106
Received: 28 September 2018 / Revised: 19 October 2018 / Accepted: 29 October 2018 / Published: 1 November 2018
(This article belongs to the Special Issue Attosecond Science and Technology: Principles and Applications)
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 medium 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. View Full-Text
Keywords: high-order harmonic generation; 3D non-adiabatic model; simulation; artificial neural network; prediction high-order harmonic generation; 3D non-adiabatic model; simulation; artificial neural network; prediction
Show Figures

Figure 1

MDPI and ACS Style

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop