Geodesic
Recursive neural network as a high-speed plate collision emulator
Čelâbinskij fiziko-matematičeskij žurnal, Tome 9 (2024) no. 1, pp. 134-143

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Based on a database obtained using a high-speed plate impact model that relates impact parameters and material model parameters to the free surface velocity profile, the study compares the learning process and accuracy of a feedforward artificial neural network and a recursive neural network. A recursive neural network provides a significantly greater accuracy and requires less training time. Using a recursive neural network as a fast model emulator and Bayesian calibration can make it possible to solve the inverse problem of determining the substance model parameters from the free surface velocity profile with a greater accuracy.
Keywords: recursive neural network, artificial neural network, artificial neural network training, high-speed plate collision. 
@article{CHFMJ_2024_9_1_a10,
     author = {V. V. Pogorelko and A. E. Mayer and E. V. Fedorov},
     title = {Recursive neural network as a high-speed plate collision emulator},
     journal = {\v{C}el\^abinskij fiziko-matemati\v{c}eskij \v{z}urnal},
     pages = {134--143},
     publisher = {mathdoc},
     volume = {9},
     number = {1},
     year = {2024},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/CHFMJ_2024_9_1_a10/}
}
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V. V. Pogorelko; A. E. Mayer; E. V. Fedorov. Recursive neural network as a high-speed plate collision emulator. Čelâbinskij fiziko-matematičeskij žurnal, Tome 9 (2024) no. 1, pp. 134-143. http://geodesic.mathdoc.fr/item/CHFMJ_2024_9_1_a10/