A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling
Abstract
Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectivelymore »
- Authors:
-
[1];
[2];
[1];
[3];
[4];
[1];
[1]
- Nanjing Univ. (China). Key Lab. of Surficial Geochemistry of Ministry of Education, School of Earth Sciences and Engineering
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
- Florida State Univ., Tallahassee, FL (United States). Dept. of Scientific Computing
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Natural Science Foundation of China (NSFC)
- OSTI Identifier:
- 1430618
- Alternate Identifier(s):
- OSTI ID: 1414960
- Grant/Contract Number:
- AC05-00OR22725; U1503282; 41672229; SC0008272; 155232
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Water Resources Research
- Additional Journal Information:
- Journal Volume: 53; Journal Issue: 12; Journal ID: ISSN 0043-1397
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES
Citation Formats
Mo, Shaoxing, Lu, Dan, Shi, Xiaoqing, Zhang, Guannan, Ye, Ming, Wu, Jianfeng, and Wu, Jichun. A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling. United States: N. p., 2017.
Web. doi:10.1002/2017WR021622.
Mo, Shaoxing, Lu, Dan, Shi, Xiaoqing, Zhang, Guannan, Ye, Ming, Wu, Jianfeng, & Wu, Jichun. A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling. United States. https://doi.org/10.1002/2017WR021622
Mo, Shaoxing, Lu, Dan, Shi, Xiaoqing, Zhang, Guannan, Ye, Ming, Wu, Jianfeng, and Wu, Jichun. Wed .
"A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling". United States. https://doi.org/10.1002/2017WR021622. https://www.osti.gov/servlets/purl/1430618.
@article{osti_1430618,
title = {A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling},
author = {Mo, Shaoxing and Lu, Dan and Shi, Xiaoqing and Zhang, Guannan and Ye, Ming and Wu, Jianfeng and Wu, Jichun},
abstractNote = {Global sensitivity analysis (GSA) and uncertainty quantification (UQ) for groundwater modeling are challenging because of the model complexity and significant computational requirements. To reduce the massive computational cost, a cheap-to-evaluate surrogate model is usually constructed to approximate and replace the expensive groundwater models in the GSA and UQ. Constructing an accurate surrogate requires actual model simulations on a number of parameter samples. Thus, a robust experimental design strategy is desired to locate informative samples so as to reduce the computational cost in surrogate construction and consequently to improve the efficiency in the GSA and UQ. In this study, we develop a Taylor expansion-based adaptive design (TEAD) that aims to build an accurate global surrogate model with a small training sample size. TEAD defines a novel hybrid score function to search informative samples, and a robust stopping criterion to terminate the sample search that guarantees the resulted approximation errors satisfy the desired accuracy. The good performance of TEAD in building global surrogate models is demonstrated in seven analytical functions with different dimensionality and complexity in comparison to two widely used experimental design methods. The application of the TEAD-based surrogate method in two groundwater models shows that the TEAD design can effectively improve the computational efficiency of GSA and UQ for groundwater modeling.},
doi = {10.1002/2017WR021622},
journal = {Water Resources Research},
number = 12,
volume = 53,
place = {United States},
year = {Wed Dec 27 00:00:00 EST 2017},
month = {Wed Dec 27 00:00:00 EST 2017}
}
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