Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach
We propose a new method to solve eigenvalue problems for linear and semilinear second order differential operators in high dimensions based on deep neural networks. The eigenvalue problem is reformulated as a fixed point problem of the semigroup flow induced by the operator, whose solution can be represented by Feynman-Kac formula in terms of forward-backward stochastic differential equations. The method shares a similar spirit with diffusion Monte Carlo but augments a direct approximation to the eigenfunction through neural-network ansatz. The criterion of fixed point provides a natural loss function to search for parameters via optimization. Our approach is able to provide accurate eigenvalue and eigenfunction approximations in several numerical examples, including Fokker-Planck operator and the linear and nonlinear Schrödinger operators in high dimensions.
Duke Scholars
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- Applied Mathematics
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- 49 Mathematical sciences
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- 09 Engineering
- 02 Physical Sciences
- 01 Mathematical Sciences
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Related Subject Headings
- Applied Mathematics
- 51 Physical sciences
- 49 Mathematical sciences
- 40 Engineering
- 09 Engineering
- 02 Physical Sciences
- 01 Mathematical Sciences