| Autor: |
Gherman, Ana Maria Mihaela, Kovács, Katalin, Cristea, Mircea Vasile, Toșa, Valer |
| Předmět: |
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| Zdroj: |
Applied Sciences (2076-3417); Nov2018, Vol. 8 Issue 11, p2106, 15p |
| Abstrakt: |
Featured Application: We present a method based on artificial neural networks—as an efficient procedure—aimed to predict the outcome of high-order harmonic generation experiments with previously unexplored parameters. The main goal is to provide quick help for attosecond science laboratories in designing experiments. 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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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