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Model Differentiation

In: Model Calibration and Parameter Estimation

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ne-Zheng Sun & Alexander Sun, 2015. "Model Differentiation," Springer Books, in: Model Calibration and Parameter Estimation, edition 127, chapter 5, pages 141-184, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4939-2323-6_5
    DOI: 10.1007/978-1-4939-2323-6_5
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