Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions

• URL: http://arxiv.org/abs/2007.05384v2
• Date: Mon, 17 Aug 2020 11:53:24 GMT
• Title: Deep neural network approximation for high-dimensional elliptic PDEs with boundary conditions
• Authors: Philipp Grohs and Lukas Herrmann
• Abstract summary: Deep neural networks are capable of approximating solutions to a class of parabolic partial differential equations without incurring the curse of dimension. The present paper considers an important such model problem, namely the Poisson equation on a domain $Dsubset mathbbRd$ subject to Dirichlet boundary conditions. It is shown that deep neural networks are capable of representing solutions of that problem without incurring the curse of dimension.
• Score: 6.079011829257036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent work it has been established that deep neural networks are capable of approximating solutions to a large class of parabolic partial differential equations without incurring the curse of dimension. However, all this work has been restricted to problems formulated on the whole Euclidean domain. On the other hand, most problems in engineering and the sciences are formulated on finite domains and subjected to boundary conditions. The present paper considers an important such model problem, namely the Poisson equation on a domain $D\subset \mathbb{R}^d$ subject to Dirichlet boundary conditions. It is shown that deep neural networks are capable of representing solutions of that problem without incurring the curse of dimension. The proofs are based on a probabilistic representation of the solution to the Poisson equation as well as a suitable sampling method.

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