Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion

• URL: http://arxiv.org/abs/2411.03384v1
• Date: Tue, 05 Nov 2024 18:11:25 GMT
• Title: Solving stochastic partial differential equations using neural networks in the Wiener chaos expansion
• Authors: Ariel Neufeld, Philipp Schmocker,
• Abstract summary: We numerically solve partial differential equations (SPDEs) numerically by using (possibly random) neural networks in the truncated Wiener chaos expansion of their corresponding solution. We provide some approximation rates for learning the solution of SPDEs with additive and/or multiplicative noise.
• Score: 3.3379026542599934
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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