|
June 6-10, 2011
| Organizers: |
|
Clint N. Dawson
|
Institute for Computational
Engineering and Sciences,
University of Texas at Austin |
|
Omar Ghattas
|
Institute for Computational
Engineering and Sciences, University of Texas at Austin |
|
Luis Tenorio
|
Mathematical and Computer Sciences,
Colorado School of Mines |
|
Karen E. Willcox
|
Aeronautics and Astronautics,
Massachusetts Institute
of Technology |
Description:
Many classes of problems in computational science and
engineering are characterized
by a cycle of experiment design, observation, parameter/state
estimation, prediction, and
decision-making. The critical steps in this process involve:
(1) modeling of the physical
processes via, for example, PDEs; (2) estimating unknown
parameters in the model from
observational data via solution of an inverse problem; (3)
propagation of input uncertainties
through the model to issue predictions; and (4) determination
of an optimal control or
decision-making strategy that takes into account the uncertain
outputs. The estimation of
unknown model parameters or state from observational data,
together with a model linking
inputs to outputs, constitutes an inverse problem; it is called
a statistical inverse problem
when at least one of the components in this process is modeled
as random. Data assimilation
and joint inversion are two particular settings that have a
wide range of applications.
In many cases of current scientific and industrial interest,
solution of the statistical in-
verse problem remains prohibitive, particularly for
high-dimensional parameter spaces and
expensive forward models. The use of established numerical and
statistical methods that
have become routine for small or moderate-sized problems poses
challenges for large-scale
problems. Yet despite this difficulty, there is a crucial need
for the development of scalable
algorithms for the solution of large-scale statistical inverse
problems: uncertainty estimation
in model parameters and state is an important precursor to the
quantification of uncertainties
underpinning prediction and decision-making. While complete
quantification of uncertainty
in inverse problems for large-scale nonlinear systems has been
often intractable, several re-
cent developments are making it viable: (1) the maturing state
of algorithms and software for
forward simulation for many classes of problems; (2) the
arrival of the petascale computing
era; (3) new advances in Bayesian computing; and (4) the
explosion of available observational
data in many scientific areas.
This workshop will assess the current state-of-the-art and
identify needs and opportunities for future research at the
intersection of large-scale inverse problems and uncertainty
quantification. It will bring together and cross-fertilize the
perspectives of researchers in the areas of inverse problems
and data assimilation, statistics, large-scale optimization,
applied and computational mathematics, high performance
computing, and forefront applications. The goal of the
workshop will be to identify promising future directions for
resolving the difficulties associated with high-dimensional
statistical inverse problems, and opportunities in such areas
as the aerospace, astrophysical, biomedical, chemical, energy,
geological, industrial, mechanical, and petroleum engineering
and sciences. Participants will be solicited broadly from
academia, government laboratories, and industry.
LIST OF CONFIRMED PARTICIPANTS
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