DeepSets and their derivative networks for solving symmetric PDEs
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DOI: 10.1007/s10915-022-01796-w
Note: View the original document on HAL open archive server: https://hal.science/hal-03154116v2
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References listed on IDEAS
- Martin Hutzenthaler & Arnulf Jentzen & Thomas Kruse & Tuan Anh Nguyen, 2020. "A proof that rectified deep neural networks overcome the curse of dimensionality in the numerical approximation of semilinear heat equations," Partial Differential Equations and Applications, Springer, vol. 1(2), pages 1-34, April.
- Huyên Pham & Xavier Warin & Maximilien Germain, 2021. "Neural networks-based backward scheme for fully nonlinear PDEs," Partial Differential Equations and Applications, Springer, vol. 2(1), pages 1-24, February.
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More about this item
Keywords
Permutation-invariant PDEs; symmetric neural networks; exchangeability; deep backward scheme; mean-field control;All these keywords.
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2022-02-14 (Big Data)
- NEP-CMP-2022-02-14 (Computational Economics)
- NEP-HIS-2022-02-14 (Business, Economic and Financial History)
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