Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media
- We present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM alsoWe present a stochastic deep collocation method (DCM) based on neural architecture search (NAS) and transfer learning for heterogeneous porous media. We first carry out a sensitivity analysis to determine the key hyper-parameters of the network to reduce the search space and subsequently employ hyper-parameter optimization to finally obtain the parameter values. The presented NAS based DCM also saves the weights and biases of the most favorable architectures, which is then used in the fine-tuning process. We also employ transfer learning techniques to drastically reduce the computational cost. The presented DCM is then applied to the stochastic analysis of heterogeneous porous material. Therefore, a three dimensional stochastic flow model is built providing a benchmark to the simulation of groundwater flow in highly heterogeneous aquifers. The performance of the presented NAS based DCM is verified in different dimensions using the method of manufactured solutions. We show that it significantly outperforms finite difference methods in both accuracy and computational cost.…
Document Type: | Article |
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Author: | Prof. Dr.-Ing. Timon RabczukORCiDGND, Hongwei Guo, Xiaoying Zhuang, Pengwan Chen, Naif AlajlanORCiD |
DOI (Cite-Link): | https://doi.org/10.1007/s00366-021-01586-2Cite-Link |
URN (Cite-Link): | https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20220209-45835Cite-Link |
URL: | https://link.springer.com/article/10.1007/s00366-021-01586-2 |
Parent Title (English): | Engineering with Computers |
Publisher: | Springer |
Place of publication: | London |
Language: | English |
Date of Publication (online): | 2022/02/07 |
Date of first Publication: | 2022/01/18 |
Release Date: | 2022/02/09 |
Publishing Institution: | Bauhaus-Universität Weimar |
Institutes and partner institutions: | Fakultät Bauingenieurwesen / Institut für Strukturmechanik (ISM) |
Volume: | 2022 |
Pagenumber: | 26 |
First Page: | 1 |
Last Page: | 26 |
Tag: | deep learning; neural architecture search; randomized spectral representation |
GND Keyword: | Maschinelles Lernen; Neuronales Lernen; Fehlerabschätzung |
Dewey Decimal Classification: | 500 Naturwissenschaften und Mathematik |
BKL-Classification: | 31 Mathematik / 31.80 Angewandte Mathematik |
Licence (German): | Creative Commons 4.0 - Namensnennung (CC BY 4.0) |