Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
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arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914278612664320 |
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| author | Neufeld, Ariel Schmocker, Philipp |
| author_facet | Neufeld, Ariel Schmocker, Philipp |
| contents | In this paper, we solve stochastic partial differential equations (SPDEs) numerically by using (possibly random) neural networks in the truncated Wiener chaos expansion of their corresponding solution. Moreover, we provide some approximation rates for learning the solution of SPDEs with additive and/or multiplicative noise. Finally, we apply our results in numerical examples to approximate the solution of three SPDEs: the stochastic heat equation, the Heath-Jarrow-Morton equation, and the Zakai equation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_03384 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion Neufeld, Ariel Schmocker, Philipp Machine Learning Numerical Analysis Probability In this paper, we solve stochastic partial differential equations (SPDEs) numerically by using (possibly random) neural networks in the truncated Wiener chaos expansion of their corresponding solution. Moreover, we provide some approximation rates for learning the solution of SPDEs with additive and/or multiplicative noise. Finally, we apply our results in numerical examples to approximate the solution of three SPDEs: the stochastic heat equation, the Heath-Jarrow-Morton equation, and the Zakai equation. |
| title | Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion |
| topic | Machine Learning Numerical Analysis Probability |
| url | https://arxiv.org/abs/2411.03384 |