期刊
JOURNAL OF COMPUTATIONAL PHYSICS
卷 425, 期 -, 页码 -出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jcp.2020.109821
关键词
Boundary layer; Numerical oscillations; Neural network; Summation-by-parts; Penalty terms; Coarse grids
Training a neural network to predict the shape of the solution can reduce numerical oscillations, increase accuracy, and efficiency.
Unresolved gradients produce numerical oscillations and inaccurate results. The most straightforward solution to such a problem is to increase the resolution of the computational grid. However, this is often prohibitively expensive and may lead to ecessive execution times. By training a neural network to predict the shape of the solution, we show that it is possible to reduce numerical oscillations and increase both accuracy and efficiency. Data from the neural network prediction is imposed using multiple penalty terms inside the domain. (C) 2020 Elsevier Inc. All rights reserved.
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