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Physics-Informed Neural Networks (PINNs) have shown continuous and increasing promise in approximating partial differential equations (PDEs), although they remain constrained by the curse of dimensionality.
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Vemuri, S.K., Büchner, T., Denzler, J.: Estimating soil hydraulic parameters for unsaturated flow using physics-informed neural networks. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) Computational Science – ICCS 2024. pp. 338–351. Springer Nature Switzerland, Cham (2024). https://doi.org/10.1007/978-3-031-63759-9_37
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Zeng, C., Burghardt, T., Gambaruto, A.M.: Feature mapping in physics-informed neural networks (pinns) (2024)
2024
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