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Upcoming Seminars

Seminars are held Tuesdays and Thursdays in POB 6.304 from 3:30-5:00 pm, unless otherwise noted. Speakers include scientists, researchers, visiting scholars, potential faculty, and ICES/UT Faculty or staff. Everyone is welcome to attend. Refreshments are served at 3:15 pm.

 

ICES Seminar
Wednesday, Jul 26, 2017 from 3:30PM to 5PM
POB 6.304

Enabling next-generation combustion simulations by intelligent integration
by Kyle Niemeyer

Assistant Professor, School of Mechanical, Industrial, and Manufacturing Engineering, Oregon State University

Details

Combustion simulations with finite-rate chemistry rely on accurate and efficient methods for solving stiff ordinary differential equations (ODEs). In a typical reacting-flow solver, the ODEs involving chemical kinetics at each spatial location are decoupled by operator splitting, allowing each to be solved concurrently. Efficient ODE solvers must take into account both numerical efficiency as well as the available thread and instruction-level parallelism of the underlying computational hardware being used to perform the simulations, especially on many-core coprocessors. This talk will summarize work on complementary efforts to reduce the computational expense of chemical kinetics on modern processing architectures. First, I will examine the performance and behavior of exponential and implicit Runge-Kutta integrators implemented for graphics processing units (GPUs). Second, I will compare the performance of explicit Runge-Kutta and implicit Rosenbrock solvers implemented using both single instruction, multiple thread and single instruction, multiple data paradigms executed on multicore CPUs, Many Integrated Core, and GPU processors. Third, I will discuss efforts to intelligently select appropriate integrators based on local stiffness. I will then make overall conclusions based on a synthesis of the results, and identify remaining open questions and directions for future research. Lastly, I will discuss other ongoing research in my group.

Bio
Dr. Kyle Niemeyer is an Assistant Professor in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University. He received his PhD in Mechanical Engineering from Case Western Reserve University in 2013. Dr. Niemeyer’s research interests include computational modeling of reacting and non-reacting fluid flows at various scales and levels of fidelity; analysis, reduction, and validation of chemical kinetic models; and GPU computing. He is also an ardent advocate of open science.

Hosted by Robert Moser





 

ICES Seminar
Thursday, Aug 10, 2017 from 3:30PM to 5PM
POB 6.304

Particle filters for spatially extended systems
by Alexandre Thiery

Assistant Professor, Department of Statistics & Applied Probability, National University of Singapore

Details

Data assimilation is the process of combining mathematical models with collected observations in order to make forecasts and quantify the associated uncertainty. In this talk, we will focus on the sequential nature of many DA problems; this context naturally leads to the repeated application of Bayes formula. The particle filter algorithm is a Monte-Carlo based approach that allows a straightforward numerical implementation of these recursive updates. Particle filters rely on importance sampling combined with a re-sampling step in order to propagate a set of particles forward in time; contrarily to other methods such as the Ensemble Kalman Filter (EnKF), particle filters do not rely on Gaussian assumptions and are asymptotically exact. Although consistent, in order to give reliable results (i.e. avoid collapse), particle filters typically require a number of particles that scale exponentially quickly with the (effective) dimension of the state-space; traditional particle filters are consequently unusable for many large scale applications. To make progress, the spatial decorrelation that is inherent to many applied scenarios has to be exploited through localization procedures. In this talk, we review some of the techniques that have recently been developed for this purpose and propose some new extensions.

Bio:
Alex Thiery is an assistant professor in the department of Statistics & Applied Probability at the National University of Singapore (NUS); before joining NUS, he obtained his PhD from Warwick University (UK). Alex is interested in leveraging Monte-Carlo and variational methods for high-dimensional Bayesian inference, with particular focus on inverse problems and Data-Assimilation.

Hosted by Tan Bui-Thanh





 

ICES Seminar
Thursday, Nov 2, 2017 from 3:30PM to 5PM
POB 6.304

Posterior error control in Bayesian Inverse Problems
by J Andrés Christen

Centro de Investigación en Matemáticas, Guanajuato, México

Details

In the Bayesian analysis of Inverse Problems most relevant cases the forward maps are defined in terms of a system of (O, P)DE's that involve numerical solvers. These then lead to a numerical/approximate posterior distribution. Recently several results have been published on the regularity conditions required on such numerical methods to ensure converge of the numerical to the theoretical posterior. However, more practical guidelines are needed.

I present some recent results that, by using Bayes Factors and in a finite dimensional setting, one can see that the numerical posterior tends to the theoretical posterior in the same order as the numerical solver used in the forward map. Moreover, when error estimates are available for the solver we can use a bound on this errors, proven to lead to basically error free posteriors. That is, given that we are observing noisy data, we may tolerate an amount (relative to the data noise) of numerical error in the solver, and end up with a basically error free posterior. In this talk I will show these results, present some examples in ODEs and PDEs and comment on the generalizations to the infinite dimensional setting.

Bio
Dr J Andres Christen has a BSc in mathematics from Universidad Nacional Autonoma de Mexico (1989, UNAM, Mexico city) and a PhD in mathematics from the University of Nottingham (1994, Nottingham, UK). His expertise is in Bayesian Statistics. Dr Christen has been working on the field for more than 25 years in applied as well as theoretical aspects of the discipline. His areas of application include ecology, paleoecolgy, environmental change, bioinformatics, among others. Moreover, recently at CIMAT he has helped to create a group on the study of inverse problems using Bayesian statistics (Bayesian UQ). In particular, Dr Christen and the group at CIMAT are working on the analysis of complex biological systems, epidemiology, sound wave scattering and other applications in physics, as well as some theoretical aspects involved in the practice of Bayesian UQ. Dr Christen holds a tenure position at CIMAT (part of the national network of research centers of CONACyT) since 2003 and is a Investigador Nacional, Sistema Nacional de Investigadores level III. He is a member of the International Society for Bayesian Analysis (ISBA), participated in the Scientific Committee of ISBA 2009{2012 and was the chair of the Local Organizing Committee,
2014 ISBA World Congress. (Personal home page www.cimat.mx/~jac/. See also uq.cimat.mx for more details on the UQ group at CIMAT.)

Hosted by Tan Bui-Thanh