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Title: High-dimensional nonlinear Bayesian inference of poroelastic fields from pressure data

We investigate solution methods for large-scale inverse problems governed by partial differential equations (PDEs) via Bayesian inference. The Bayesian framework provides a statistical setting to infer uncertain parameters from noisy measurements. To quantify posterior uncertainty, we adopt Markov Chain Monte Carlo (MCMC) approaches for generating samples. To increase the efficiency of these approaches in high-dimension, we make use of local information about gradient and Hessian of the target potential, also via Hamiltonian Monte Carlo (HMC). Our target application is inferring the field of soil permeability processing observations of pore pressure, using a nonlinear PDE poromechanics model for predicting pressure from permeability. We compare the performance of different sampling approaches in this and other settings. We also investigate the effect of dimensionality and non-gaussianity of distributions on the performance of different sampling methods.

 
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Award ID(s):
1635407 2108784
NSF-PAR ID:
10395356
Author(s) / Creator(s):
 ;  ;  ;  
Publisher / Repository:
SAGE Publications
Date Published:
Journal Name:
Mathematics and Mechanics of Solids
Volume:
28
Issue:
9
ISSN:
1081-2865
Format(s):
Medium: X Size: p. 2108-2131
Size(s):
["p. 2108-2131"]
Sponsoring Org:
National Science Foundation
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