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This content will become publicly available on December 27, 2018

Title: A Taylor Expansion-Based Adaptive Design Strategy for Global Surrogate Modeling With Applications in Groundwater Modeling

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 » improve the computational efficiency of GSA and UQ for groundwater modeling.« less
ORCiD logo [1] ; ORCiD logo [2] ; ORCiD logo [1] ; ORCiD logo [3] ; ORCiD logo [4] ; ORCiD logo [1] ; ORCiD logo [1]
  1. Nanjing Univ. (China). Key Lab. of Surficial Geochemistry of Ministry of Education, School of Earth Sciences and Engineering
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computational Sciences and Engineering Division
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
  4. Florida State Univ., Tallahassee, FL (United States). Dept. of Scientific Computing
Publication Date:
Grant/Contract Number:
AC05-00OR22725; U1503282; 41672229; SC0008272; 155232
Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 53; Journal Issue: 12; Journal ID: ISSN 0043-1397
American Geophysical Union (AGU)
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21); National Natural Science Foundation of China (NNSFC)
Country of Publication:
United States
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1414960