Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Autoren

  • HW Guo
  • XY Zhuang
  • Pengwan Chen
  • N Alajlan
  • T Rabczuk

Organisationseinheiten

Externe Organisationen

  • Tongji University
  • Beijing Institute of Technology
  • King Saud University
Forschungs-netzwerk anzeigen

Details

OriginalspracheEnglisch
Seiten (von - bis)5173-5198
Seitenumfang26
FachzeitschriftEngineering with computers
Jahrgang38
Ausgabenummer6
Frühes Online-Datum18 Jan. 2022
PublikationsstatusVeröffentlicht - Dez. 2022

Abstract

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 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.

ASJC Scopus Sachgebiete

Zitieren

Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. / Guo, HW; Zhuang, XY; Chen, Pengwan et al.
in: Engineering with computers, Jahrgang 38, Nr. 6, 12.2022, S. 5173-5198.

Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review

Guo HW, Zhuang XY, Chen P, Alajlan N, Rabczuk T. Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with computers. 2022 Dez;38(6):5173-5198. Epub 2022 Jan 18. doi: 10.1007/s00366-021-01586-2
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title = "Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media",
abstract = "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 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.",
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Download

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AU - Guo, HW

AU - Zhuang, XY

AU - Chen, Pengwan

AU - Alajlan, N

AU - Rabczuk, T

N1 - Funding Information: The authors extend their appreciation to the Distinguished Scientist Fellowship Program (DSFP) at King Saud University for funding this work.

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KW - ALGORITHM

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