A MODULAR APPROACH TO SIMULATION WITH AUTOMATIC SENSITIVITY CALCULATION
When using simulation codes, one often has the task of minimizing a scalar objective function with respect to numerous parameters. This situation occurs when trying to fit (assimilate) data or trying to optimize an engineering design. For simulations in which the objective function to be minimized is reasonably well behaved, that is, is differentiable and does not contain too many multiple minima, gradient-based optimization methods can reduce the number of function evaluations required to determine the minimizing parameters. However, gradient-based methods are only advantageous if one can efficiently evaluate the gradients of the objective function. Adjoint differentiation efficiently provides these sensitivities. One way to obtain code for calculating adjoint sensitivities is to use special compilers to process the simulation code. However, this approach is not always so ''automatic''. We will describe a modular approach to constructing simulation codes, which permits adjoint differentiation to be incorporated with relative ease.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- US Department of Energy (US)
- DOE Contract Number:
- W-7405-ENG-36
- OSTI ID:
- 774512
- Report Number(s):
- LA-UR-01-802; TRN: AH200121%%83
- Resource Relation:
- Conference: Conference title not supplied, Conference location not supplied, Conference dates not supplied; Other Information: PBD: 1 Feb 2001
- Country of Publication:
- United States
- Language:
- English
Similar Records
Calibration of elastoplastic constitutive model parameters from full-field data with automatic differentiation-based sensitivities
Inversion based on computational simulations