Inversion based on complex computational simulations
Experimental data often can only be interpreted by means of a computational simulation that approximately models the physical situation. The authors will discuss techniques that facilitate application to complex, large-scale simulations of the standard approach to inversion in which gradient-based optimization is used to find the parameters that bet match the data. The fundamental enabling techniques are adjoint differentiation to efficiently compute the gradient of an objective function with respect to all the variables of a simulation and relatively new gradient-based optimization algorithms. 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.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 548752
- Report Number(s):
- LA-UR-97-2846; CONF-9709117-; ON: DE98000806; TRN: AHC29724%%155
- Resource Relation:
- Conference: IEEE workshop on nonlinear signal of image processing, Mackinac, MI (United States), 8-10 Sep 1997; Other Information: PBD: [1997]
- Country of Publication:
- United States
- Language:
- English
Similar Records
Progress Report, December 2010: Improved Site Characterization And Storage Prediction Through Stochastic Inversion Of Time-Lapse Geophysical And Geochemical Data
Source Inversion for contaminant plume dispersion in urban environments using building-resolving simulations