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<!DOCTYPE html>
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<title>Rocky Mountain Summer Workshop on Uncertainty Quantification</title>
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<h1>Rocky Mountain Summer Workshop on Uncertainty Quantification</h1>
<h2>University of Colorado Denver</h2>
<h3>July 15-17, 2015</h3>
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<p>The University of Colorado Denver is hosting a summer workshop on uncertainty quantification from July 15-17, 2015. This intensive three-day workshop will provide hands-on training on the mathematical theory behind and application of state-of-the-art open source software and computational tools for the quantification of uncertainty and a posteriori error estimation in physics-based models.</p>
<p>Participants will have an unique opportunity to personally interact with several of the leading researchers in this emerging area of computational mathematics and predictive science. Approximately half of each day is devoted to the practical application of the theory and participants will leave this workshop with many computational examples and software that may serve as a template for future research projects.</p>
<h3>Registration Information</h3>
<p>This workshop is aimed primarily at graduate students and postdoctoral researchers, but all applications for registration will be considered. Space is limited and support for travel will only be provided for graduate students and/or postdoctoral researchers on an as-needed basis. All decisions on registration and support will be made final within two weeks of the deadline for travel support on April 3, 2015. If your travel will be supported by other means, please indicate this on your cover letter in order to reserve your spot prior to the deadline.
<p>We anticipate providing full travel support for up to 20 participants. To apply for full or partial travel support and/or to reserve your spot for this workshop send a cover letter explaining your interest in the workshop, your CV, and a letter of recommendation to <a href="mailto: [email protected]">[email protected]</a>. The deadline for consideration of travel support is April 3, 2015.</p>
<h3>Speakers and Reference Material</h3>
<ul>
<li>
<p><a href="http://users.ices.utexas.edu/~omar/">Dr. Omar Ghattas</a> (Director, Center for Computational Geosciences, ICES, UT-Austin) and collaborators <a href = "http://math.nyu.edu/~stadler/">Georg Stadler </a> (Courant Institute of Mathematical Sciences, New York University) and <a href = "http://faculty.ucmerced.edu/npetra/">Noemi Petra</a> (School of Natural Sciences, University of California, Merced) will present research on the modeling and quantification of uncertainties in ice sheet modeling. </p>
<p>Some good references for these talks include
<ul>
<li>T. Isaac, N. Petra, G. Stadler, O. Ghattas, <a href="http://dx.doi.org/10.1016/j.jcp.2015.04.047">Scalable and efficient
algorithms for the propagation of uncertainty from data through
inference to prediction for large-scale problems, with application to
flow of the Antarctic ice sheet</a>, Journal of Computational Physics,
296(1):348–368, 2015.</li>
<li>N. Petra, H. Zhu, G. Stadler, T.J.R. Hughes, O. Ghattas, <a href="http://dx.doi.org/10.3189/2012JoG11J182">An inexact
Gauss-Newton method for inversion of basal sliding and rheology
parameters in a nonlinear Stokes ice sheet model</a>, Journal of
Glaciology, 58(211):889–903, 2012.</li>
<li>T. Bui-Thanh, O. Ghattas, J. Martin, and G. Stadler, <a href="http://dx.doi.org/10.1137/12089586X">A computational
framework for infinite-dimensional Bayesian inverse problems. Part I:
The linearized case, with applications to global seismic inversion.</a>
SIAM Journal on Scientific Computing, 35(6):A2494-A2523, 2013.</li>
<li>N. Petra, J. Martin, G. Stadler, and O. Ghattas, <a href="http://dx.doi.org/10.1137/130934805">A computational
framework for infinite-dimensional Bayesian inverse problems. Part II:
Stochastic Newton MCMC with application to ice sheet flow inverse
problems</a>, SIAM Journal on Scientific Computing, 36(4):A1525–A1555,
2014.</li>
</ul>
</p>
</li>
<li>
<p><a href="http://chg.ices.utexas.edu/index.html">Dr. Clint Dawson</a> (Head of the Computational Hydraulics Group, ICES, UT-Austin) and collaborators <a href = "http://www.math.ucdenver.edu/~tbutler/"> Troy Butler </a> (Department of Mathematical and Statistical Sciences, University of Colorado Denver) and Steve Mattis (ICES, UT-Austin) will present research on the modeling and quantification of uncertainties in storm surge and contaminant transport modeling.</p>
<p> Some references for these talks include </p>
<ul>
<li>T. Bulter, D. Estep, S. Tavener, C. Dawson, J. J. Westerink,
A measure-theoretic computational method for inverse sensitivity problems III:
Multiple quantities of interest. SIAM/ASA Journal on Uncertainty Quantification, 2:1, 174-202.
<a href="http://epubs.siam.org/doi/pdf/10.1137/130930406">
http://epubs.siam.org/doi/pdf/10.1137/130930406</a></li><p>
<li>T. Butler, L. Graham, D. Estep, C. Dawson, J.J. Westerink, Definition and solution of a
stochastic inverse problem for the Manning’s n parameter field in hydrodynamic models,
Advances in Water Resources, Available online 3 February 2015, ISSN 0309-1708,
<a href="http://dx.doi.org/10.1016/j.advwatres.2015.01.011">
doi:10.1016/j.advwatres.2015.01.011</a>.</li><p>
<li>T. Butler, D. Estep, S. Tavener, T. Wildey, C. Dawson, and L. Graham, Solving stochastic inverse
problems using sigma-algebras on contour maps, 2014, <a href="http://arxiv.org/pdf/1407.3851">
arXiv preprint arXiv:1407.3851</a></li><p>
<li>D. R. Harp, V. V. Vesselinov, Contaminant remediation decision analysis using information gap
theory, Stochastic Environmental Research and Risk Assessment, 27, 159-168, 2013.
<a href="http://dx.doi.org/10.1007/s00477-012-0573-1">doi:10.1007/s00477-012-0573-1</a></li><p>
<li>S. Bunya, J. C. Dietrich, J. J. Westerink, B. A. Ebersole, J. M. Smith, J. H. Atkinson,
R. Jensen, D. T. Resio, R. A. Luettich, C. Dawson, V. J. Cardone, A. T. Cox, M. D. Powell,
H. J. Westerink, and H. J. Roberts, 2010: A High-Resolution Coupled Riverine flow, tide, wind,
Wind Wave, and Storm Surge Model for Southern Louisiana and Mississippi. Part I: Model
Development and Validation. Mon. Wea. Rev., 138, 345–377.
<a href="http://journals.ametsoc.org/doi/abs/10.1175/2009MWR2906.1">
doi: http://dx.doi.org/10.1175/2009MWR2906.1</a></li><p>
<li>The Official ADCIRC Web Site <a href="http://adcirc.org/">http://adcirc.org/</a></li><p>
</ul>
</li>
<li>
<p><a href="http://www.me.utexas.edu/directory/faculty/moser/robert/131/">Dr. Robert Moser</a> (Director, Center for Predictive Engineering and Computational Sciences (PECOS), ICES, UT-Austin) and PECOS researchers
Damon McDougall and Varis Carey will present research on the modeling and quantification of uncertainties in physical engineering applications. Damon McDougall will also give the QUESO minitutorial on Thursday, July 16. </p>
<p>Some good references for these talks and the QUESO minitutorial include </p>
<ul>
<li><a href="http://users.ices.utexas.edu/~damon/imperial_talk_mcdougall.pdf">Bayesian parameter estimation in predictive engineering</a></li>
<li><a href="http://arxiv.org/abs/1507.00398">The Parallel C++ Statistical Library for Bayesian Inference: QUESO</a></li>
<li><a href="https://github.com/libqueso/queso/blob/dev/QUESO_users_manual.pdf">The QUESO user's manual</a></li>
</ul>
</li>
<li>
<p><a href="http://www.stat.colostate.edu/~estep/">Dr. Don Estep</a> (Director, Center for Interdisciplinary Mathematics and Sciences, CSU) will give a minitutorial on adjoint based a posteriori error estimation for physics-based models. </p>
<p>Some good references for this minitutorial include
<ul>
<li><a href="http://www.stat.colostate.edu/~estep/research/preprints/adjointcourse_final.pdf">Notes on Adjoints</a></li>
<li><a href="http://www.stat.colostate.edu/~estep/research/talks/montrealcourse.pdf">Slides from a previous short course in Montreal</a></li>
<li><a href="http://www.stat.colostate.edu/~estep/research/talks/estep_minitutorial.pdf">Slides from a SIAM minitutorial</a></li>
</ul>
</p>
</li>
</ul>
<h3>Sponsors</h3>
Thank you to the university sponsors for making this workshop free of charge to graduate student and postdoctoral participants. This workshop is sponsored by several University of Colorado Denver sources.
<ul>
<li>
<p>
Sponsored in part by the University of Colorado Denver Graduate School
</p>
</li>
<li>
<p>
Sponsored in part by the University of Colorado College of Liberal Arts and Sciences
</p>
</li>
<li>
<p>
Sponsored in part by the Department of Mathematical and Statistical Sciences, University of Colorado Denver
</p>
</li>
<li>
<p>
Sponsored in part by the Center for Computational Mathematics, University of Colorado Denver
</p>
</li>
</ul>
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