Survey of multifidelity methods in uncertainty propagation, inference, and optimization
B Peherstorfer, K Willcox, M Gunzburger - Siam Review, 2018 - SIAM
In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …
models are available that describe a system of interest. These different models have varying …
Geological realism in hydrogeological and geophysical inverse modeling: A review
Scientific curiosity, exploration of georesources and environmental concerns are pushing
the geoscientific research community toward subsurface investigations of ever-increasing …
the geoscientific research community toward subsurface investigations of ever-increasing …
Learning physics-based models from data: perspectives from inverse problems and model reduction
This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …
inverse problems and model reduction. These fields develop formulations that integrate data …
The cost-accuracy trade-off in operator learning with neural networks
The termsurrogate modeling'in computational science and engineering refers to the
development of computationally efficient approximations for expensive simulations, such as …
development of computationally efficient approximations for expensive simulations, such as …
[图书][B] Active subspaces: Emerging ideas for dimension reduction in parameter studies
PG Constantine - 2015 - SIAM
Parameter studies are everywhere in computational science. Complex engineering
simulations must run several times with different inputs to effectively study the relationships …
simulations must run several times with different inputs to effectively study the relationships …
[图书][B] Data assimilation: methods, algorithms, and applications
This book places data assimilation (DA) into the broader context of inverse problems and the
theory, methods, and algorithms that are used for their solution. It strives to provide a …
theory, methods, and algorithms that are used for their solution. It strives to provide a …
A computational framework for infinite-dimensional Bayesian inverse problems Part I: The linearized case, with application to global seismic inversion
We present a computational framework for estimating the uncertainty in the numerical
solution of linearized infinite-dimensional statistical inverse problems. We adopt the …
solution of linearized infinite-dimensional statistical inverse problems. We adopt the …
An introduction to full waveform inversion
Full waveform inversion (FWI) is a high-resolution seismic imaging technique that is based
on using the entire content of seismic traces for extracting physical parameters of the …
on using the entire content of seismic traces for extracting physical parameters of the …
Image reconstruction in electrical impedance tomography based on structure-aware sparse Bayesian learning
Electrical impedance tomography (EIT) is developed to investigate the internal conductivity
changes of an object through a series of boundary electrodes, and has become increasingly …
changes of an object through a series of boundary electrodes, and has become increasingly …
Bayesian inference with optimal maps
TA El Moselhy, YM Marzouk - Journal of Computational Physics, 2012 - Elsevier
We present a new approach to Bayesian inference that entirely avoids Markov chain
simulation, by constructing a map that pushes forward the prior measure to the posterior …
simulation, by constructing a map that pushes forward the prior measure to the posterior …