ML-Approach for Modeling Viscoelastic and Physically Nonlinear Materials Based on Symbolic Regression
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-61415-6_14
Reference14 articles.
1. Viazovychenko, Y., Larin, O.: Stochastic Optimization Algorithms for Data Processing in Experimental Self-heating Process. In: Nechyporuk, M., Pavlikov, V., Kritskiy, D. (eds.) Integrated Computer Technologies in Mechanical Engineering - 2020. ICTM 2020. Lecture Notes in Networks and Systems, vol. 188, pp. 644–653. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-66717-7_55
2. Abdellaoui, I.A., Mehrkanoon, S.: Symbolic regression for scientific discovery: an application to wind speed forecasting. In: 2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2021). https://doi.org/10.1109/ssci50451.2021.9659860
3. Abdusalamov, R., Kaplunov, J., Itskov, M.: Discovering asymptotic expansions for problems in mechanics using symbolic regression. Mech. Res. Commun. 133, 104197 (2023). https://doi.org/10.1016/j.mechrescom.2023.104197
4. Hernandez, A., Balasubramanian, A., Yuan, F., Mason, S.A.M., Mueller, T.: Fast, accurate, and transferable many-body interatomic potentials by symbolic regression. NPJ Comput. Mater. 5(1), 112 (2019). https://doi.org/10.1038/s41524-019-0249-1
5. Keren, L.S., Liberzon, A., Lazebnik, T.: A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge. Sci. Rep. 13, 1249 (2023). https://doi.org/10.1038/s41598-023-28328-2
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