An inverse problem strategy based on forward model evaluations: Gradient-based optimization without adjoint solves

Conference · Fri Jul 01 00:00:00 EDT 2016

OSTI ID:1250450

Aguilo Valentin, Miguel Alejandro ^[1]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

This study presents a new nonlinear programming formulation for the solution of inverse problems. First, a general inverse problem formulation based on the compliance error functional is presented. The proposed error functional enables the computation of the Lagrange multipliers, and thus the first order derivative information, at the expense of just one model evaluation. Therefore, the calculation of the Lagrange multipliers does not require the solution of the computationally intensive adjoint problem. This leads to significant speedups for large-scale, gradient-based inverse problems.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000
• OSTI ID: 1250450
• Report Number(s): SAND--2016-0736J; 618889
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
97 MATHEMATICS AND COMPUTING
Newton’s method
compliance error minimization
gradient-based optimization
heat transfer
inverse problem
nonlinear conjugate gradient