Details
Originalsprache | Englisch |
---|---|
Seiten (von - bis) | 5173-5198 |
Seitenumfang | 26 |
Fachzeitschrift | Engineering with computers |
Jahrgang | 38 |
Ausgabenummer | 6 |
Frühes Online-Datum | 18 Jan. 2022 |
Publikationsstatus | Veröffentlicht - Dez. 2022 |
Abstract
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Ingenieurwesen (insg.)
- Informatik (insg.)
- Angewandte Informatik
- Mathematik (insg.)
- Modellierung und Simulation
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in: Engineering with computers, Jahrgang 38, Nr. 6, 12.2022, S. 5173-5198.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
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TY - JOUR
T1 - Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media
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.
PY - 2022/12
Y1 - 2022/12
N2 - 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.
AB - 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.
KW - Deep learning
KW - Neural architecture search
KW - Error estimation
KW - Randomized spectral representation
KW - Method of manufactured solutions
KW - Log-normally distributed
KW - Physics-informed
KW - Sensitivity analysis
KW - Hyper-parameter optimization algorithms
KW - Transfer learning
KW - PARTIAL-DIFFERENTIAL-EQUATIONS
KW - GLOBAL SENSITIVITY-ANALYSIS
KW - FLOW SIMULATION
KW - RANDOM-FIELDS
KW - TRANSPORT
KW - ALGORITHM
KW - NETWORKS
KW - POROSITY
KW - LAW
UR - http://www.scopus.com/inward/record.url?scp=85123125774&partnerID=8YFLogxK
U2 - 10.1007/s00366-021-01586-2
DO - 10.1007/s00366-021-01586-2
M3 - Article
VL - 38
SP - 5173
EP - 5198
JO - Engineering with computers
JF - Engineering with computers
SN - 0177-0667
IS - 6
ER -