A Bayesian Approach to Estimating Background Flows from a Passive Scalar

Details

• Personal Author:
  Borggaard J ; Glatt-Holtz N ; Krometis J
• Description:
  We consider the statistical inverse problem of estimating a background flow field (e.g., of air or water) from the partial and noisy observation of a passive scalar (e.g., the concentration of a solute), a common experimental approach to visualizing complex fluid flows. Here the unknown is a vector field that is specified by a large or infinite number of degrees of freedom. Since the inverse problem is ill-posed, i.e., there may be many or no background flows that match a given set of observations, we regularize it by laying out a functional analytic and Bayesian framework for approaching this problem. In doing so, we leverage substantial recent advances in statistical inference and adjoint methods for infinite-dimensional problems. We then identify interesting example problems that exhibit posterior measures with simple and complex structure. We use these examples to conduct a large-scale benchmark of Markov chain Monte Carlo methods developed in recent years for infinite-dimensional settings. Our results indicate that these methods are capable of resolving complex multimodal posteriors in high dimensions. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety
• Keywords:
  Author Keywords: Bayesian Statistical Inversion Fluids Fluid Turbulence Markov Chain Monte Carlo Mathematical Models MCMC Passive Scalars Quantitative Analysis Statistical Analysis Statistical Quality Control
• ISSN:
  2166-2525
• Document Type:
  Text
• Funding:
  Contract
• Genre:
  Journal Article
• Place as Subject:
  Louisiana ; OSHA Region 3 ; OSHA Region 6 ; Virginia
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  8
• Issue:
  3
• NIOSHTIC Number:
  nn:20068200
• Citation:
  SIAM/ASA J Uncertain Quantif 2020 Jul; 8(3):1036-1060
• Contact Point Address:
  Jef Borggaard, Department of Mathematics, Virginia Tech, Blacksburg, VA 24061
• Email:
  jborggaard@vt.edu
• Federal Fiscal Year:
  2020
• Performing Organization:
  Virginia Polytechnic Institute & State University
• Peer Reviewed:
  True
• Start Date:
  20140901
• Source Full Name:
  SIAM/ASA Journal on Uncertainty Quantification
• End Date:
  20190831
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:7d6df92783a921db360fad3b93c600560175c9038ad53932589babe231f042f30f66c6efd7772352b61d3e41c5d8517cdc06fdec1f3bb17e405955ff9a1d39bf
• Download URL:
  https://stacks.cdc.gov/view/cdc/230406/cdc_230406_DS1.pdf
• File Type:
  [PDF - 8.47 MB ]