Artificial Neural Network Trained to Predict High-Harmonic Flux
Source abstract
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 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.
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Research context
OpenAlex topics: Laser-Matter Interactions and Applications · Spectroscopy Techniques in Biomedical and Chemical Research · Advanced Fiber Laser Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
National Institute for R&D of Isotopic and Molecular Technologies, Donat str. 67-103, 400293 Cluj-Napoca, Romania
Chemical Engineering Department, Babeș-Bolyai University, Arany János str. 11, 400028 Cluj-Napoca, Romania
Funding
Ministerul Educației și Cercetării Științifice
References (67)
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