Multilevel CNNs for parametric PDEs based on adaptive finite elements
Authors
- Eigel, Martin
ORCID: 0000-0003-2687-4497 - Schütte, Janina
ORCID: 0009-0000-9924-3229
2020 Mathematics Subject Classification
- 35R60 65N22 65J10 68T07 68T05 65N15 65N30 65N50 65N55
Keywords
- Parametric PDE, adaptive finite element method, multilevel decomposition, neural network, approximation
DOI
Abstract
A neural network architecture is presented that exploits the multilevel properties of high-dimensional parameter-dependent partial differential equations, enabling an efficient approximation of parameter-to-solution maps, rivaling best-in-class methods such as low-rank tensor regression in terms of accuracy and complexity. The neural network is trained with data on adaptively refined finite element meshes, thus reducing data complexity significantly. Error control is achieved by using a reliable finite element a posteriori error estimator, which is also provided as input to the neural network. par The proposed U-Net architecture with CNN layers mimics a classical finite element multigrid algorithm. It can be shown that the CNN efficiently approximates all operations required by the solver, including the evaluation of the residual-based error estimator. In the CNN, a culling mask set-up according to the local corrections due to refinement on each mesh level reduces the overall complexity, allowing the network optimization with localized fine-scale finite element data. par A complete convergence and complexity analysis is carried out for the adaptive multilevel scheme, which differs in several aspects from previous non-adaptive multilevel CNN. Moreover, numerical experiments with common benchmark problems from Uncertainty Quantification illustrate the practical performance of the architecture.
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