Events

Mar 2 - Mar 9, 2014

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  Mathematical, Computational and Theoretical Epidemiology: Challenges and Opportunities

  By Prof. Carlos Castillo-Chavez, Regents Professor and Joaquin Bustoz Jr. Professor of Mathematical Biology at Arizona State University

  Mar 3, 15:00 - 16:00

  B9 H1

  The marriage of mathematics and epidemics has a long and distinguished history with a plethora of successes that go back to the work of Daniel Bernoulli (1700 – 1782) and Nobel Laureate and physician Sir Ronald Ross (1911) and associates. These individuals, mostly physicians, created the field of mathematical epidemiology in their efforts to meet their commitment to diminish health disparities, the consequences of poverty and the lack of access to health services.

Feb 23 - Mar 2, 2014

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  Exact Sampling and Inference for Non-Linear Stochastic Differential Equations. Short course By Prof. Alexandros Beskos (Associate Professor at NUS, Singapore)

  Prof. Alexandros Beskos, Associate Professor at NUS, Singapore

  Feb 27, 15:00 - 16:30

  B1 R4214

  Exact Sampling and Inference for Non-Linear Stochastic Differential Equations. I will present a surprisingly simple algorithm we developed some years ago for the exact simulation of non-linear diffusion processes. Exact sampling of such processes was considered impossible for such models. The presence of stochasticity allows for developing a rejection sampling algorithm by proposing candidate Brownian paths; the accept/reject rule then involves events of an appropriate Poisson process. We have thus termed the method the Weiner-Poisson factorization of a non-linear SDE. The developed sampling method provides also a natural approach for carrying out maximum-likelihood-based inference for unknown parameters of the SDE.
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  Sequential Monte Carlo Samplers for Applications in High Dimensions Short course By Prof. Alexandros Beskos (Associate Professor at NUS, Singapore)

  Prof. Alexandros Beskos

  Feb 26, 09:00 - Sep 18, 10:30

  B1 R4214

  Sequential Monte Carlo (SMC) methods are nowadays routinely applied in a variety of complex applications: hidden Markov models, dynamical systems, target tracking, control problems, just to name a few. Whereas SMC methods have been greatly refined in the last decades and are now much better understood, they are still known to suffer from the curse of dimensionality: algorithms can sometimes break down exponentially fast with the dimension of the state space. As a consequence, practitioners in high-dimensional Data Assimilation applications in atmospheric sciences, oceanography and elsewhere will typically use 3D-Var or Kalman-filter-type approximations that will provide biased estimates in the presence of non-linear model dynamics.
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  Mean Field Games and Mean Field Type Control By Prof. Alain Bensoussan (Ashbel Smith Professor and Director of the International Center for Decision and Risk Analysis (ICDRiA), University of Texas at Dallas, USA)

  Prof. Alain Bensoussan, Ashbel Smith Professor and Director of the International Center for Decision and Risk Analysis (ICDRiA), University of Texas at Dallas, USA

  Feb 25, 14:00 - 15:00

  B1 R4214

  This is a joint work with Professor Jens Frehse and Professor Phillip Yam. We give a unified presentation of two research domains which have been very popular in the recent years, but considered independently. We provide a Dynamic Programming as well as a Stochastic Maximum Principle approach. We discuss many extensions and the issue of time consistency. Extended slides will be provided.

Feb 16 - Feb 23, 2014

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  Data Assimilation - Part 2

  Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group

  Feb 20, 15:00 - 17:30

  B3 R5520

  data assimilation

  Please register in advance at: Registration See please the program here: Schedule​ See lecture notes here: Notes​ See Melanie Ades notes - Part I: Slides See MATLAB code here: Code See Mealine Ades notes - Part II: Slides ​See Kody Law notes here: Slides​
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  Data Assimilation - Part 1

  Kody J. H. Law, Senior Research Scientist, Stochastic Numerics Research Group

  Feb 17, 09:00 - 11:15

  B3 R5520

  data assimilation

  Please register in advance at: Registration See please the program here: Schedule​ See lecture notes here: Notes​ See Melanie Ades notes - Part I: Slides See MATLAB code here: Code See Mealine Ades notes - Part II: Slides ​See Kody Law notes here: Slides​

Feb 9 - Feb 16, 2014

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  Game Theory Meets Computer Science and Engineering by Dr. Hamidou Tembine

  Hamidou Tembine, Senior Research Scientist, Stochastic Numerics Research Group

  Feb 11, 11:00 - 12:00

  B9 R4222

  Game Theory has been a staple of economics research since 1950, when John Nash who is the subject of the movie A Beautiful Mind, published the seminal paper that would win him the Nobel Prize in economics. As game theory has matured, it’s become even more central to the field of economics and social sciences.
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  Uncertainty quantification in Discrete Fracture Network models By Prof. Claudio Canuto (Dipartimento di Scienze Matematiche, Politecnico di Torino, Italy)

  Prof. Claudio Canuto, Polytechnic of Turin, Italy

  Feb 10, 15:00 - 16:00

  B1 R4214

  discrete fracture network

  Discrete Fracture Network (DFN) models are widely used in the simulation of subsurface flows; they describe a geological reservoir as a system of many intersecting planar polygons representing the underground network of fractures. Among the different approaches to DFN simulations, recently (Berrone et al, 2013) a numerical model has been formulated as a PDE-constrained optimization problem, in which neither fracture/fracture nor fracture/trace mesh conformity is required.

Feb 2 - Feb 9, 2014

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  Overview of numerical methods for quantification of uncertainties by Dr. Alexander Litvinenko

  Alexander Litvinenko, Senior Research Scientist, Stochastic Numerics Research Group

  Feb 4, 11:00 - 12:00

  B9 R4222

  In this talk, I will explain what is uncertainty quantification and how to model uncertainties via random variables/random fields. I will give several examples of stochastic partial differential equations with uncertain coefficients, uncertain computations domain, uncertain right-hand side, or boundary conditions.

Jan 26 - Feb 9, 2014

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  WEP 2014 - Distributed Strategic Learning by Dr. Hamidou Tembine

  Hamidou Tembine, Senior Research Scientist, Stochastic Numerics Research Group

  Feb 2, 11:00 - 12:00

  B9 R4222

  This course will introduce the audience to some of the essential ingredients of learning in games under uncertainty (random matrix games), particularly reinforcement learning, cost-of-learning, Q-learning, mean-field learning, combined learning, heterogeneous learning and hybrid learning.

Jan 5 - Jan 12, 2014

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  Advances in Uncertainty Quantification Methods, Algorithms and Applications (UQAW 2014)

  Raul Tempone, Professor, Applied Mathematics and Computational Sciences

  Jan 6, 09:00 - Jan 10, 17:00

  B4 B5 A0215

  uncertainty quantification algorithms

  ​SRI UQ Annual Meeting 6-10 January 2014 See please the program here: schedule Technical presentations slide: here Full Book of Abstracts: here Full Book of Abstracts with Posters: here​ ​(40 MB)

Nov 24 - Dec 1, 2013

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  Hybrid Adaptive Multilevel Monte Carlo Algorithm for Non-Smooth Observables of It ô Stochastic Differential Equations by Nadhir Ben Rached

  Nadhir Ben Rached, Ph.D., Applied Mathematics and Computational Sciences

  Nov 27, 10:30 - 12:00

  B1 R4214

  The Monte Carlo forward Euler method with uniform time stepping is the standard technique to compute an approximation of the expected payoff of a solution of an Itô SDE. For a given accuracy requirement TOL, the complexity of this technique for well-behaved problems, that is the amount of computational work to solve the problem, is O(TOL-3).
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  Discontinuous Galerkin approximation to the Vlasov-Poisson system By Dr. Blanca Ayuso de Dios (Centre de Recerca Matematica at Barcelona) ​

  Dr. Blanca Ayuso de Dios, Centre de Recerca Matematica at Barcelona

  Nov 25, 15:00 - 16:00

  B1 R4214

  Partial Differential Equations

  One of the simplest model problems in the kinetic theory of plasma--physics is the Vlasov-Poisson system with periodic boundary conditions. Such system describes the evolution of a plasma of charged particles (electrons and ions) under the effects of the transport and self-consistent electric field. In this talk, we present a family of discontinuous Galerkin (DG) methods for the approximation of the Vlasov-Poisson system.

Oct 27 - Nov 3, 2013

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  A Sample from the Stochastic Numerics Group by Prof. Raul Tempone

  Raul Tempone, Professor, Applied Mathematics and Computational Sciences

  Oct 31, 12:00 - 13:00

  B9 L2 R2322 H1

  I will provide a quick glance into the research of my group, giving particular emphasis on the activities of my KAUST students. This will hopefully give a connection between our research and our educational activities.

Sep 29 - Oct 6, 2013

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  An Introduction to Stochastic Control Theory By Prof. Boualem Djehiche (KTH Royal Institute of Technology, Stockholm, Sweden)

  Prof. Boualem Djehiche, KTH Royal Institute of Technology, Stockholm, Sweden

  Oct 1, 15:30 - Oct 6, 17:00

  B1 R4214

  The course treats completely observed control problems with a state equation of Itô type and with a performance function which is the expected value of a functional of the controlled process. As is well known, Bellman's dynamic programming leading to the celebrated Hamilton-Jacobi-Bellman equation and Pontryagin's maximum principle are the most commonly used approaches to solving the control problem.

Apr 21 - Apr 28, 2013

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  SRI UQ Annual Workshop 2013

  Raul Tempone, Professor, Applied Mathematics and Computational Sciences

  Apr 26, 09:00 - Apr 30, 17:00

  Auditorium L0 Between B4 and B5

  uncertainty quantification

  Apr 26 2013 9:00 AM - Apr 30 2013 5:00 PM SRI UQ Annual Meeting 26-30 April 2013 Schedule_2013 To view the WORKSHOP report please click on this link: Report

Mar 24 - Mar 31, 2013

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  Bayesian Analysis of Stochastic Process Models (AMCS 390) By Prof. Fabrizio Ruggeri (Italian National Research Council, Milano, Italy)

  Prof. Fabrizio Ruggeri, Italian National Research Council, Milano, Italy

  Mar 25, 10:30 - Apr 24, 12:00

  B9 R2132

  The student will be introduced to Bayesian modeling in selected, but relevant, stochastic processes and their applications: Markov chains, Poisson processes, reliability and queues. The use of real examples will be helpful in understanding why and how perform a Bayesian analysis. Students will be asked to analyze real data, from the elicitation of priors and modeling to (numerical) computation of estimates and forecasts and interpretation of findings.

Mar 17 - Mar 31, 2013

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  Ensemble Kalman Methods For Inverse Problems By Prof. Andrew Stuart (Warwick University, United Kingdom)

  Prof. Andrew Stuart, Warwick University, United Kingdom

  Mar 24, 15:00 - 16:00

  B1 R4214

  Many problems in the physical sciences require the determination of an unknown field from a finite set of indirect measurements. Examples include oceanography, oil recovery, water resource management and weather forecasting. This may be formulated as a least squares problem to match the model output to the data. I will demonstrate that ideas from the Ensemble Kalman Filter can be adapted to solve such problems: by running multiple interacting copies of the model, and exposing their output to the (suitably randomized) data, a derivative-free minimization tool is constructed.

Mar 10 - Mar 17, 2013

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  Uncertainty Quantification in Molecular Dynamics By Dr. Francesco Rizzi (John Hopkins University, USA)

  Dr. Francesco Rizzi, John Hopkins University, USA

  Mar 13, 15:00 - 16:00

  B1 R3119

  uncertainty quantification

  Molecular dynamics (MD) is widely employed in both industrial and academic environments, playing a key role in the study of a large variety of systems, ranging fromliquids to solids, as well as biomolecules, such as proteins and nucleic acids (DNA, RNA). MD simulations provide a suitable and convenient method to explore dynamical properties of a system at the atomic level which, in general, aresignificantly difficult and expensive to investigate in experimental settings.

Mar 3 - Mar 10, 2013

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  Parametric Problems, Stochastic, and Identification By Prof. Hermann Matthies (ISCTUB, Germany)

  Prof. Hermann Matthies, Institute of Scientific Computing TU Braunschweig, Geramany

  Mar 6, 15:00 - 16:00

  B1 R4102

  Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily incorporating new information, e. g. through a measurement, by connecting it to Bayes's theorem. The unknown quantity is modelled as a (may be high-dimensional) random variable. Such a description has two constituents, the measurable function and the measure.

Feb 10 - Feb 17, 2013

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  Accurate Filtering With 3DVAR For Dissipative Systems By Dr. Kody Law (University of Warwick, United Kingdom)

  Dr. Kody Law, Postdoctoral Research Fellow, University of Warwick, United Kingdom

  Feb 11, 15:00 - 16:00

  B1 R4214

  Unstable dynamical systems can be stabilized, and hence the solution recovered from noisy data, provided two conditions hold. First, observe enough of the system: the unstable modes. Second, weight the observed data sufficiently over the model. In this talk I will illustrate this for the 3DVAR filter applied to three dissipative dynamical systems of increasing dimension: the Lorenz 1963 model, the Lorenz 1996 model, and the 2D Navier-Stokes equation.

Nov 25 - Dec 9, 2012

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  PDE with Random Coefficients As A Problem in High-Dimensional Numerical Integration By Prof. Ian H. Sloan (University of New South Wales, Australia)

  Prof. Ian H. Sloan, University of New South Wales, Australia

  Dec 2, 15:00

  B1 R4214

  Partial Differential Equations

  This talk is concerned with the use of quasi-Monte Carlo methods combined with finite-element methods to handle an elliptic PDE with a random field as a coefficient. A number of groups have considered such problems (under headings such as polynomial chaos, stochastic Galerkin and stochastic collocation) by reformulating them as deterministic problems in a high dimensional parameter space, where the dimensionality comes from the number of random variables needed to characterize the random field.

Nov 11 - Nov 18, 2012

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  On Generalized Fiducial Inference By Prof. Jan Hannig (Univ. of Carolina at Chapel Hill, USA)

  Prof. Jan Hannig University of North Carolina at Chapel Hill

  Nov 13, 16:30 - 17:30

  B1 R4214

  R. A. Fisher's fiducial inference has been the subject of many discussions and controversies ever since he introduced the idea during the 1930's. The idea experienced a bumpy ride, to say the least, during its early years and one can safely say that it eventually fell into disfavor among mainstream statisticians. However, it appears to have made a resurgence recently under various names and modifications.
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  Rethinking Uncertainty Quantification By Prof. Edward Boone

  Prof. Edward Boone, Associate Professor, Department of Statistical Sciences and Operations Research

  Nov 12, 15:00 - 16:00

  B1 R4214

  Model Validation is often a difficult task with many different objectives and their associated metrics. Many times these metrics can address a single issue such as model fit, predictive performance, uncertainty quantification, etc. One quantity of interest is the amount of uncertainty associated with model misspecification. In this talk we present a bootstrap based method for uncertainty quantification under model misspecification.

Nov 4 - Nov 18, 2012

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  Bayesian Estimation Of Thermal Conductivity in Polymethyl Methacrylate By Prof. Fabrizio Ruggeri (Italian National Research Council, Milano, Italy)

  Prof. Fabrizio Ruggeri, Italian National Research Council, Milano, Italy

  Nov 11, 15:00 - 16:00

  B1 R4214

  Bayesian approach is developed for estimating the thermal conductivity of a homogeneous material from the temperature evolution acquired in few internal points. Temperature evolution is described by the classical one-dimensional heat equation, in which the thermal conductivity is one of the coefficients.

Nov 4 - Nov 11, 2012

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  Approximation of Stochastic PDE’s Involving White Noises By Prof. Hassan Manouzi (Laval University, Quebec, Canada)

  Prof. Hassan Manouzi, Applied Mathematics, Laval University, Quebec, Canada

  Nov 5, 15:00 - 16:00

  B1 R2418

  Stochastic partial differential equations (SPDEs) play fundamental and important roles in many problems and their numerical simulations become useful and important in practical applications. The mathematical treatment of SPDEs represents however a greater challenge in comparison to deterministic partial differential equations.

Sep 30 - Oct 7, 2012

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  Gaussian Beam Approximations of High Frequency By Prof. Olof Runborg (KTH, Sweden)

  Prof. Olof Runborg, Royal Institute of Technology (KTH), Stockholm, Sweden

  Oct 2, 15:00 - 16:00

  B1 R4214

  Gaussian beams are asymptotically valid high frequency solutions to hyperbolic partial differential equations, concentrated on a curve through the domain. Superpositions of Gaussian beams provide a tool to generate more general high frequency solutions. In contrast to the standard geometrical optics, the Gaussian beam approximation does not break down at caustics.

Sep 23 - Sep 30, 2012

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  Covariance Eigenproblems and their Numerical Treatment By Prof. Oliver Ernst (Technical University Bergakademie Freiberg, Germany) ​

  Prof. Oliver Ernst, Technical University Bergakademie Freiberg, Germany

  Sep 26, 15:00 - 16:00

  B1 R4214

  One of the most versatile parametrizations of stochastic processes or random fields for large-scale computations is provided by the Karhunen-Loève expansion. Its computational realization requires (at least the partial) solution of an eigenvalue problem for a covariance operator.

Sep 16 - Sep 23, 2012

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  Sharp Complexity Bounds For Computing Quadrature Formulas for Marginal Distribution of SDEs By Prof. Thomas Mueller (University of Passau, Germany)

  Prof. Thomas Mueller, University of Passau, Germany

  Sep 17, 15:00 - 16:00

  B1 R4214

  We consider the problem of approximating the marginal distribution of the solution of a stochastic differential equation (SDE) by probability measures with finite support, i.e., by quadrature formulas with positive weights summing up to one. We study deterministic algorithms in a worst case analysis with respect to classes of SDEs, which are defined in terms of smoothness constraints for the coefficients of the equation.

Sep 9 - Sep 23, 2012

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  The Multi-Level Monte Carlo Technique for Approximation of Distribution Functions and an Application to AF4 By Prof. Klaus Ritter (Technische Universität Kaiserslautern, Germany)

  Prof. Klaus Ritter, Technische Universität Kaiserslautern

  Sep 16, 15:00 - 16:00

  B1 R4214

  The multi-level Monte Carlo approach is a powerful variance reduction technique, which is applied, in particular, in the context of SDEs. While the standard task is to compute the expectation of a real-valued functional, we discuss how to approximate a distribution function on a compact interval. In this talk, we establish upper bounds for the error of suitable multi-level algorithms.