Inversion based on computational simulations

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A standard approach to solving inversion problems that involve many parameters uses gradient-based optimization to find the parameters that best match the data. The authors discuss enabling techniques that facilitate application of this approach to large-scale computational simulations, which are the only way to investigate many complex physical phenomena. Such simulations may not seem to lend themselves to calculation of the gradient with respect to numerous parameters. However, adjoint differentiation allows one to efficiently compute the gradient of an objective function with respect to all the variables of a simulation. When combined with advanced gradient-based optimization algorithms, adjoint differentiation permits ... continued below

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15 p.

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Hanson, K.M.; Cunningham, G.S. & Saquib, S.S. September 1, 1998.

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Description

A standard approach to solving inversion problems that involve many parameters uses gradient-based optimization to find the parameters that best match the data. The authors discuss enabling techniques that facilitate application of this approach to large-scale computational simulations, which are the only way to investigate many complex physical phenomena. Such simulations may not seem to lend themselves to calculation of the gradient with respect to numerous parameters. However, adjoint differentiation allows one to efficiently compute the gradient of an objective function with respect to all the variables of a simulation. When combined with advanced gradient-based optimization algorithms, adjoint differentiation permits one to solve very large problems of optimization or parameter estimation. These techniques will be illustrated through the simulation of the time-dependent diffusion of infrared light through tissue, which has been used to perform optical tomography. The techniques discussed have a wide range of applicability to modeling including the optimization of models to achieve a desired design goal.

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15 p.

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INIS; OSTI as DE98006313

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  • Maximum entropy and Bayesian methods workshop, Boise, ID (United States), 4-7 Aug 1997

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  • Other: DE98006313
  • Report No.: LA-UR--98-998
  • Report No.: CONF-9708174--
  • Grant Number: W-7405-ENG-36
  • Office of Scientific & Technical Information Report Number: 666040
  • Archival Resource Key: ark:/67531/metadc712031

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Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • September 1, 1998

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • Feb. 25, 2016, 7:57 p.m.

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Hanson, K.M.; Cunningham, G.S. & Saquib, S.S. Inversion based on computational simulations, article, September 1, 1998; New Mexico. (digital.library.unt.edu/ark:/67531/metadc712031/: accessed August 20, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.