Asymptotic Physics-Informed Neural Networks for Solving Singularly Perturbed Problems
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-4390-2_2
Reference28 articles.
1. Arzani, A., Cassel, K.W., D’Souza, R.M.: Theory-guided physics-informed neural networks for boundary layer problems with singular perturbation. J. Comput. Phys. 473, 111768 (2023)
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3. Berkhahn, S., Ehrhardt, M.: A physics-informed neural network to model covid-19 infection and hospitalization scenarios. Adv. Continuous Discrete Models 2022(1), 61 (2022)
4. Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877–1901 (2020)
5. Cao, F., Gao, F., Guo, X., Yuan, D.: Physics-informed neural networks with parameter asymptotic strategy for learning singularly perturbed convection-dominated problem. Comput. Math. Appli. 150, 229–242 (2023)
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