Optimal Experimental Design Using a Consistent Bayesian Approach
- Univ. of Colorado, Denver, CO (United States). Dept. of Mathematical and Statistical Sciences
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
We consider the utilization of a computational model to guide the optimal acquisition of experimental data to inform the stochastic description of model input parameters. Our formulation is based on the recently developed consistent Bayesian approach for solving stochastic inverse problems, which seeks a posterior probability density that is consistent with the model and the data in the sense that the push-forward of the posterior (through the computational model) matches the observed density on the observations almost everywhere. Given a set of potential observations, our optimal experimental design (OED) seeks the observation, or set of observations, that maximizes the expected information gain from the prior probability density on the model parameters. We discuss the characterization of the space of observed densities and a computationally efficient approach for rescaling observed densities to satisfy the fundamental assumptions of the consistent Bayesian approach. Finally, numerical results are presented to compare our approach with existing OED methodologies using the classical/statistical Bayesian approach and to demonstrate our OED on a set of representative partial differential equations (PDE)-based models.
- Research Organization:
- Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC04-94AL85000; NA0003525
- OSTI ID:
- 1478379
- Report Number(s):
- SAND-2017-4749J; 666529
- Journal Information:
- ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems. Part B. Mechanical Engineering, Vol. 4, Issue 1; ISSN 2332-9017
- Publisher:
- American Society of Mechanical EngineersCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Goal-oriented optimal design of experiments for large-scale Bayesian linear inverse problems
|
journal | July 2018 |
| Goal-Oriented Optimal Design of Experiments for Large-Scale Bayesian Linear Inverse Problems | text | January 2018 |
Efficient D-Optimal Design of Experiments for Infinite-Dimensional Bayesian Linear Inverse Problems
|
journal | January 2018 |
| Optimal Experimental Design for Constrained Inverse Problems | preprint | January 2017 |
| Optimum Experimental Design for Interface Identification Problems | text | January 2018 |
| Learning Quantities of Interest from Dynamical Systems for Observation-Consistent Inversion | preprint | January 2020 |
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