Final Report: Large-Scale Optimization for Bayesian Inference in Complex Systems
The SAGUARO (Scalable Algorithms for Groundwater Uncertainty Analysis and Robust Optimiza- tion) Project focuses on the development of scalable numerical algorithms for large-scale Bayesian inversion in complex systems that capitalize on advances in large-scale simulation-based optimiza- tion and inversion methods. Our research is directed in three complementary areas: efficient approximations of the Hessian operator, reductions in complexity of forward simulations via stochastic spectral approximations and model reduction, and employing large-scale optimization concepts to accelerate sampling. Our efforts are integrated in the context of a challenging testbed problem that considers subsurface reacting flow and transport. The MIT component of the SAGUARO Project addresses the intractability of conventional sampling methods for large-scale statistical inverse problems by devising reduced-order models that are faithful to the full-order model over a wide range of parameter values; sampling then employs the reduced model rather than the full model, resulting in very large computational savings. Results indicate little effect on the computed posterior distribution. On the other hand, in the Texas-Georgia Tech component of the project, we retain the full-order model, but exploit inverse problem structure (adjoint-based gradients and partial Hessian information of the parameter-to- observation map) to implicitly extract lower dimensional information on the posterior distribution;more »
- Publication Date:
- OSTI Identifier:
- 1113343
- Report Number(s):
- DOE-UTAUSTIN-25860
- DOE Contract Number:
- FG02-08ER25860
- Resource Type:
- Technical Report
- Research Org:
- The University of Texas at Austin
- Sponsoring Org:
- USDOE SC Office of Advanced Scientific Computing Research (SC-21)
- Contributing Orgs:
- MIT
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; optimization, inverse problems, Bayesian inference, subsurface flows
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