1. D3M: A Deep Domain Decomposition Method for Partial Differential Equations
- Author
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Tianfan Wu, Ke Li, Qifeng Liao, and Kejun Tang
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Optimization problem ,General Computer Science ,Computer science ,010103 numerical & computational mathematics ,01 natural sciences ,Machine Learning (cs.LG) ,Variational principle ,FOS: Mathematics ,Applied mathematics ,General Materials Science ,Mathematics - Numerical Analysis ,Domain decomposition ,0101 mathematics ,mesh-free ,Partial differential equation ,Artificial neural network ,business.industry ,Deep learning ,General Engineering ,deep learning ,Domain decomposition methods ,Numerical Analysis (math.NA) ,010101 applied mathematics ,Exact solutions in general relativity ,physics-constrained ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,parallel computation ,PDEs ,business ,lcsh:TK1-9971 - Abstract
A state-of-the-art deep domain decomposition method (D3M) based on the variational principle is proposed for partial differential equations (PDEs). The solution of PDEs can be formulated as the solution of a constrained optimization problem, and we design a multi-fidelity neural network framework to solve this optimization problem. Our contribution is to develop a systematical computational procedure for the underlying problem in parallel with domain decomposition. Our analysis shows that the D3M approximation solution converges to the exact solution of underlying PDEs. Our proposed framework establishes a foundation to use variational deep learning in large-scale engineering problems and designs. We present a general mathematical framework of D3M, validate its accuracy and demonstrate its efficiency with numerical experiments.
- Published
- 2020