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Metric-based, goal-oriented mesh adaptation using machine learning
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DOI：10.1016/j.jcp.2020.109957.png)
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This paper presents a machine-learning approach for determining the optimal anisotropy in a computational mesh, in the context of an output-based adaptive solution procedure. Artificial neural networks are used to predict the desired element aspect ratio from readily accessible features of the primal and adjoint solutions. Whereas the sizing of the element is still based on an adjoint-weighted residual error estimate, the network augments this information with element stretching magnitude and direction, at lower computational and implementation costs compared to a more rigorous approach: mesh optimization through error sampling and synthesis (MOESS). The network is trained to provide anisotropy information in the form of a normalized metric field, computed from primal, adjoint, and error indicator features. MOESS-optimized meshes for a variety of steady aerodynamic flows governed by the Reynolds-averaged Navier-Stokes equations in two dimensions provide data for training several multi-layer perceptron networks, which differ in size and inputs. The networks are then deployed and tested by driving complete mesh adaptation sequences, and the results show improvements in mesh efficiency compared to pure primal Hessian-based anisotropy detection and in many cases to MOESS itself. (C) 2020 Elsevier Inc. All rights reserved.
Keyword:
Output error estimation
Neural network
Mesh optimization
Metric
Adjoint
Discontinuous Galerkin
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IF:
3.8
论文数:
1.6W
被引数:
7.4W
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