Solving Kolmogorov PDEs without the curse of dimensionality via deep learning and asymptotic expansion with Malliavin calculus
Akihiko Takahashi, Toshihiro Yamada
| Alternative title: | Asymptotic expansion and deep neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with nonlinear coefficients |
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| Year of publication: |
[2023] ; This version : May 9, 2023
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| Authors: | Takahashi, Akihiko ; Yamada, Toshihiro |
| Publisher: |
[Tokyo] : Center for Advanced Research in Finance |
| Subject: | Asymptotic expansion | Deep learning | Kolmogorov PDEs | Malliavin calculus | Curse of dimensionality | Theorie | Theory | Stochastischer Prozess | Stochastic process | Lernprozess | Learning process |
freely available
| Extent: | 1 Online-Ressource (circa 28 Seiten) |
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| Series: | CARF working paper. - Tokyo : Center for Advanced Research in Finance, ZDB-ID 3079869-3. |
| Type of publication: | Book / Working Paper |
| Type of publication (narrower categories): | Graue Literatur ; Non-commercial literature ; Arbeitspapier ; Working Paper |
| Language: | English |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014266288