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.
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Sun, NZ., Sun, A. (2015). Model Differentiation. In: Model Calibration and Parameter Estimation. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2323-6_5
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DOI: https://doi.org/10.1007/978-1-4939-2323-6_5
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Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-2322-9
Online ISBN: 978-1-4939-2323-6
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