Multiobjective Inversion and Regularization

Ne-Zheng Sun () and Alexander Sun ()
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Ne-Zheng Sun: University of California at Los Angeles, Department of Civil and Environmental Engineering
Alexander Sun: University of Texas at Austin, Bureau of Economic Geology, Jackson School of Geosciences

Chapter 3 in Model Calibration and Parameter Estimation, 2015, pp 69-105 from Springer

Abstract: Abstract After incorporating prior information, the inverse problem becomes a bi-criterion optimization problem. The use of prior information in such a way can be seen as a special case of the regularization method introduced in this chapter. Regularization provides a generalframework for increasing the stability of inverse solutions at the priceof possible loss of accuracy. EWR models are often multistatemodels characterized by a set of coupled equations. The statevariable of one equation may depend on states or parameters inother equations. This implies that measurements of one statevariable may provide information for identifying the states and/orparameters in other equations. The inversion of a multistate model iscalled a coupled inverse problem and can be solved by multiobjectiveoptimization (MOO). Various algorithms for solving MOO, includingrecently developed multiobjective evolutionary algorithms, areintroduced. These algorithms can also be used to solvemultiobjective inverse problems.

Keywords: Root Mean Square Error; Inverse Problem; Pareto Front; Inverse Solution; Average Root Mean Square Error (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-1-4939-2323-6_3

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