Analysing Family of Pareto Front-Based Evolutionary Algorithms for PINNs: A Case Study of Solving the Laplace Equation with Discontinuous Boundary Conditions-Reference-Cited by-同舟云学术

Analysing Family of Pareto Front-Based Evolutionary Algorithms for PINNs: A Case Study of Solving the Laplace Equation with Discontinuous Boundary Conditions

Published:2023 Issue: Volume: Page:239-246
ISSN:1860-949X
Container-title:Advances in Neural Computation, Machine Learning, and Cognitive Research VII
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Author:

Lazovskaya Tatiana,Tarkhov Dmitriy,Chistyakova Maria,Razumov Egor,Sergeeva Anna,Palamarchuk Veronika

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-44865-2_26

Reference8 articles.

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2. Lazovskaya, T., Malykhina, G., Tarkhov, D.: Physics-based neural network methods for solving parameterized singular perturbation problem. Computation 9, 9 (2021). https://doi.org/10.3390/computation9090097

3. Nguyen, T.N.K., Dairay, T., Meunier, R., Mougeot, M.: Physics-informed neural networks for non-Newtonian fluid thermo-mechanical problems: an application to rubber calendering process. Eng. Appl. Artif. Intell. 114, 105176 (2022). https://doi.org/10.1016/j.engappai.2022.105176

4. Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019). https://doi.org/10.1016/j.jcp.2018.10.045

5. Abualigah, L., Diabat, A.: A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Clust. Comput. 24(1), 205–223 (2020). https://doi.org/10.1007/s10586-020-03075-5