Model Differentiation

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 5 in Model Calibration and Parameter Estimation, 2015, pp 141-184 from Springer

Abstract: Abstract In the previous chapters, we showed that an inverse problem ultimately becomes an optimization problem, regardless the type of framework (deterministic or statistical) used to formulate it. Gradient-based algorithms are efficient for solving optimization problems, but require derivatives of the objective function as inputs. In this chapter, we will consider methods for obtaining derivatives of a generic function defined by a model or by a computer code.

Keywords: Sensitivity Coefficient; Reverse Mode; Adjoint Equation; Sensitivity Equation; Adjoint Problem (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4939-2323-6_5

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DOI: 10.1007/978-1-4939-2323-6_5

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