Parallelized Adaptive Importance Sampling for Solving Inverse Problems

Parallelized Adaptive Importance Sampling for Solving Inverse Problems

In the field of groundwater hydrology and more generally geophysics, solving inverse problems in a complex, geologically realistic, and discrete model space often requires the usage of Monte Carlo methods. In a previous paper we introduced PoPEx, a sampling strategy, able to handle such constraints...

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Bibliographic Details
Main Authors: Christoph Jäggli, Julien Straubhaar, Philippe Renard
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Earth Science
Subjects:
adaptive importance sampling
machine learning
uncertainty quantification
bayesian inversion
monte carlo
multiple-point statistics
Online Access:https://www.frontiersin.org/article/10.3389/feart.2018.00203/full

Internet

https://www.frontiersin.org/article/10.3389/feart.2018.00203/full

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