Data-driven uncertainty quantification for predictive subsurface flow and transport modeling

dc.contributor.advisorDawson, Clinton N.
dc.contributor.committeeMemberLandis, Chad
dc.contributor.committeeMemberBui, Tan
dc.contributor.committeeMemberGhattas, Omar
dc.creatorHe, Jiachuan
dc.date.accessioned2019-04-11T16:41:49Z
dc.date.available2019-04-11T16:41:49Z
dc.date.created2018-12
dc.date.issued2019-02-06
dc.date.submittedDecember 2018
dc.date.updated2019-04-11T16:41:49Z
dc.description.abstractSpecification of hydraulic conductivity as a model parameter in groundwater flow and transport equations is an essential step in predictive simulations. It is often infeasible in practice to characterize this model parameter at all points in space due to complex hydrogeological environments leading to significant parameter uncertainties. Quantifying these uncertainties requires the formulation and solution of an inverse problem using data corresponding to observable model responses. Several types of inverse problems may be formulated under various physical and statistical assumptions on the model parameters, model response, and the data. Solutions to most types of inverse problems require large numbers of model evaluations. In this study, we incorporate the use of surrogate models based on support vector machines to increase the number of samples used in approximating a solution to an inverse problem at a relatively low computational cost. To test the global capabilities of this type of surrogate model for quantifying uncertainties, we use a framework for constructing pullback and push-forward probability measures to study the data-to-parameter-to-prediction propagation of uncertainties under minimal statistical assumptions. Additionally, we demonstrate that it is possible to build a support vector machine using relatively low-dimensional representations of the hydraulic conductivity to propagate distributions. The numerical examples further demonstrate that we can make reliable probabilistic predictions of contaminant concentration at spatial locations corresponding to data not used in the solution to the inverse problem. This dissertation is based on the article entitled Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines by Jiachuan He, Steven Mattis, Troy Butler and Clint Dawson [32]. This material is based upon work supported by the U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program under Award Number DE-SC0009286 as part of the DiaMonD Multifaceted Mathematics Integrated Capability Center.
dc.description.departmentEngineering Mechanics
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/74257
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/1387
dc.language.isoen
dc.subjectStochastic inverse problem
dc.subjectParameter estimation
dc.subjectHydraulic conductivity
dc.subjectMeasure theory
dc.subjectSupport vector regression
dc.titleData-driven uncertainty quantification for predictive subsurface flow and transport modeling
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentEngineering Mechanics
thesis.degree.disciplineEngineering Mechanics
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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