Inverse methods in hydrogeology: Evolution and recent trends
Parameter identification is an essential step in constructing a groundwater model. The
process of recognizing model parameter values by conditioning on observed data of the …
process of recognizing model parameter values by conditioning on observed data of the …
Use of paired simple and complex models to reduce predictive bias and quantify uncertainty
J Doherty, S Christensen - Water Resources Research, 2011 - Wiley Online Library
Modern environmental management and decision‐making is based on the use of
increasingly complex numerical models. Such models have the advantage of allowing …
increasingly complex numerical models. Such models have the advantage of allowing …
Assessing leakage detectability at geologic CO2 sequestration sites using the probabilistic collocation method
We present an efficient methodology for assessing leakage detectability at geologic carbon
sequestration sites under parameter uncertainty. Uncertainty quantification (UQ) and risk …
sequestration sites under parameter uncertainty. Uncertainty quantification (UQ) and risk …
Bayesian updating via bootstrap filtering combined with data-driven polynomial chaos expansions: methodology and application to history matching for carbon dioxide …
Abstract Model calibration and history matching are important techniques to adapt
simulation tools to real-world systems. When prediction uncertainty needs to be quantified …
simulation tools to real-world systems. When prediction uncertainty needs to be quantified …
Parameter and predictive outcomes of model simplification
TA Watson, JE Doherty… - Water Resources …, 2013 - Wiley Online Library
Simplification is an unavoidable aspect of model usage. Even complex, physically based
models are simplifications of reality. More profound simplification is required to construct the …
models are simplifications of reality. More profound simplification is required to construct the …
Machine-learning-based modeling of coarse-scale error, with application to uncertainty quantification
S Trehan, LJ Durlofsky - Computational Geosciences, 2018 - Springer
The use of upscaled models is attractive in many-query applications that require a large
number of simulation runs, such as uncertainty quantification and optimization. Highly …
number of simulation runs, such as uncertainty quantification and optimization. Highly …
Prediction of permeability of porous media using optimized convolutional neural networks
Permeability is an important parameter to describe the behavior of a fluid flow in porous
media. To perform realistic flow simulations, it is essential that the fine scale models include …
media. To perform realistic flow simulations, it is essential that the fine scale models include …
Simplification error analysis for groundwater predictions with reduced order models
M Gosses, T Wöhling - Advances in water resources, 2019 - Elsevier
Groundwater resource management often requires detailed numerical models that make
calibration and predictive uncertainty analysis computationally challenging. Reduced order …
calibration and predictive uncertainty analysis computationally challenging. Reduced order …
Cokriging for multivariate Hilbert space valued random fields: application to multi-fidelity computer code emulation
In this paper we propose Universal trace co-kriging, a novel methodology for interpolation of
multivariate Hilbert space valued functional data. Such data commonly arises in multi-fidelity …
multivariate Hilbert space valued functional data. Such data commonly arises in multi-fidelity …
典型陆军部队装备维修作业能力评估系统仿真建模研究
杜海东， 曹军海， 刘福胜 - 系统仿真学报, 2020 - china-simulation.com
针对陆军部队维修作业仿真评估需求, 提出了一套仿真模型设计方案. 分析了部队维修作业能力
仿真评估原理, 进而明确了评估模型需求; 给出了维修任务生成, 维修保障系统建模方案; …
仿真评估原理, 进而明确了评估模型需求; 给出了维修任务生成, 维修保障系统建模方案; …