Adaptive Approximation Error Models for Efficient Uncertainty Quantification with Application to Multiphase Subsurface Fluid Flow

Published: Sep 10, 2018
Abstract
Sample-based Bayesian inference provides a route to uncertainty quantification in the geosciences, though is very computationally demanding in the na\ive form that requires simulating an accurate computer model at each iteration. We present a new approach that adaptively builds a stochastic model for the error induced by a reduced model. This enables sampling from the correct target distribution at reduced computational cost, while avoiding...
Paper Details
Title
Adaptive Approximation Error Models for Efficient Uncertainty Quantification with Application to Multiphase Subsurface Fluid Flow
Published Date
Sep 10, 2018
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