Deep Autoregressive Neural Networks for High‐Dimensional Inverse Problems in Groundwater Contaminant Source Identification
Author:
Affiliation:
1. Key Laboratory of Surficial Geochemistry of Ministry of Education, School of Earth Sciences and EngineeringNanjing University Nanjing China
2. Center for Informatics and Computational ScienceUniversity of Notre Dame Notre Dame IN USA
Funder
National Natural Science Foundation of China
Publisher
American Geophysical Union (AGU)
Subject
Water Science and Technology
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR024638
Reference65 articles.
1. A review of surrogate models and their application to groundwater modeling
2. Surrogate model based iterative ensemble smoother for subsurface flow data assimilation
3. Iterative filter based estimation of fully 3D heterogeneous fields of permeability and Mualem-van Genuchten parameters
4. Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother
5. Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification
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