Deep learning based numerical approximation algorithms for stochastic partial differential equations
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2020
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| _version_ | 1866912660114636800 |
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| author | Beck, Christian Becker, Sebastian Cheridito, Patrick Jentzen, Arnulf Neufeld, Ariel |
| author_facet | Beck, Christian Becker, Sebastian Cheridito, Patrick Jentzen, Arnulf Neufeld, Ariel |
| contents | In this article, we introduce and analyze a deep learning based approximation algorithm for SPDEs. Our approach employs neural networks to approximate the solutions of SPDEs along given realizations of the driving noise process. If applied to a set of simulated noise trajectories, it yields empirical distributions of SPDE solutions, from which functionals like the mean and variance can be estimated. We test the performance of the method on stochastic heat equations with additive and multiplicative noise as well as stochastic Black-Scholes equations with multiplicative noise and Zakai equations from nonlinear filtering theory. In all cases, the proposed algorithm yields accurate results with short runtimes in up to 100 space dimensions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2012_01194 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Deep learning based numerical approximation algorithms for stochastic partial differential equations Beck, Christian Becker, Sebastian Cheridito, Patrick Jentzen, Arnulf Neufeld, Ariel Numerical Analysis Machine Learning Probability In this article, we introduce and analyze a deep learning based approximation algorithm for SPDEs. Our approach employs neural networks to approximate the solutions of SPDEs along given realizations of the driving noise process. If applied to a set of simulated noise trajectories, it yields empirical distributions of SPDE solutions, from which functionals like the mean and variance can be estimated. We test the performance of the method on stochastic heat equations with additive and multiplicative noise as well as stochastic Black-Scholes equations with multiplicative noise and Zakai equations from nonlinear filtering theory. In all cases, the proposed algorithm yields accurate results with short runtimes in up to 100 space dimensions. |
| title | Deep learning based numerical approximation algorithms for stochastic partial differential equations |
| topic | Numerical Analysis Machine Learning Probability |
| url | https://arxiv.org/abs/2012.01194 |