Events

Mar 17 - Mar 23, 2019

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  On Nonlocal Keller-Segel Type Equations

  Dr. Suleyman Ulusoy, American University of Ras Al Khaimah, UAE

  Mar 20, 16:00 - 17:00

  B1 L3 R3119

  Applied Partial Differential Equations Mathematical Biology

  In the first part of the talk we investigate a Keller-Segel model with quorum sensing and a fractional diffusion operator. This model describes the collective cell movement due to chemical sensing with flux limitation for high cell densities and with anomalous media represented by a nonlinear, degenerate fractional diffusion operator. The purpose here is to introduce and prove the existence of a properly defined entropy solution. In the second part of the talk we will analyze an equation that is gradient flow of a functional related to Hardy-Littlewood-Sobolev inequality in whole Euclidean space of higher dimensions.
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  Locally Discrete Maximum Principle DG finite element method for the transport equation

  Manuel Quezada De Luna, Research Scientist, Numerical Mathematics

  Mar 20, 12:00 - 13:00

  B1 L4 R4214

  flux-corrected transport discrete maximum principle transport equation

  In this talk we propose a Flux Corrected Transport (FCT)-like stabilization suitable for high-order Discontinuous Galerkin (DG) finite element discretizations. As a model problem we consider the multi-dimensional transport equation. The method guarantees that the solution satisfies a local Discrete Maximum Principle (DMP). A solution is said to satisfy a DMP locally if any degree of freedom is bounded with respect to the solution at the previous time step in some defined neighborhood.

Mar 10 - Mar 16, 2019

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  Asymptotic properties of composites

  Rita A. Ferreira, Research Scientist, Mean-field Games and Nonlinear PDE

  Mar 14, 12:00 - 13:00

  B9 L2 R2322 H1

  gamma-convergence Asymptotic Performance Analysis

  In this talk, I will start with an overview of my research to date. Then, I will address in more detail the study of the asymptotic behavior of a two-dimensional variational model within finite crystal plasticity for high-contrast bilayered composites. More precisely, we consider materials arranged into periodically alternated thin horizontal strips of an elastically rigid component and a softer one with one active slip system. The energies arising from our modeling assumptions are of integral form, featuring linear growth and non-standard differential constraints.
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  Numerical Approximation of the Stochastic Cahn-Hilliard Equation and the (Stochastic) Hele-Shaw Problem

  Professor Lubomir Banas (Bielefeld University)

  Mar 13, 00:00

  B1 L2 R2202

  applied mathematics

  The Cahn-Hilliard equation is a fourth order parabolic partial differential equation (PDE) that is widely used as a phenomenological model to describe the evolution of interfaces in many practical problems, such as, the microstructure formation in materials, fluid flow, etc. It has been observed in the engineering literature that the stochastic version of the Cahn-Hilliard equation provides a better description of the experimentally observed evolution of complex microstructure.

Nov 11 - Nov 17, 2018

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  2018 Workshop on Statistics and Data Science

  Nov 12, 08:30 - Nov 15, 17:00

  Auditorium 0215 between Buildings 4 & 5

  data science high dimensional statistics modeling biological processes physical processes

  The Statistics Program at KAUST is proud to host the 2018 Workshop on Statistics and Data Science. This workshop gathers the leading experts on statistical data science to discuss the current needs, challenges and opportunities of modeling massive and high dimensional data, predicting complex biological and physical processes. The workshop will run from November 12-14. Talks and posters presentations will take place in Auditorium 0215 (between Buildings 4 & 5). View Workshop's Agenda.

Nov 4 - Nov 10, 2018

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  Spatio-Temporal Data Analysis by Transformed Gaussian Processes

  Yuan Yan, PhD Student of Prof. Marc Genton, KAUST

  Nov 5, 17:15 - 18:15

  B2 L5 R5220

  spatio-temporal data analysis statistics

  In the analysis of spatio-temporal data, statistical inference based on the assumption that the data follow a Gaussian distribution is ubiquitous due to its many attractive properties. However, data collected from different fields of science, such as meteorology, astronomy, biology and medicine, rarely meet the assumption of Gaussianity. One option is to apply a monotonic transformation to the data such that the transformed data have a distribution that is close to Gaussian. In this thesis, we focus on a flexible two-parameter family of transformations, the Tukey g-and-h (TGH) transformation

Oct 14 - Oct 20, 2018

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  Solution structure and high order numerical methods for time-dependent problems

  Dr. Hailiang Liu, Professor of Mathematics, Iowa State University (ISU)

  Oct 15, 16:00 - 17:00

  B1 L3 R3422

  The course discusses the solution structure and high order structure-preserving numerical methods for several time-dependent problems, modeled by partial differential equations balanced with diffusion, convection, and various interactions. The main topic focuses on the model structure and the construction of stable, accurate approximate algorithms for these problems, and the interplay between analytical theory and computational aspects of such algorithms with real applications. Here is a list of topics to be discussed: (1) Optimization, entropy and differential equations (2) Critical threshold

Mar 18 - Mar 24, 2018

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  Computational and Statistical Interface to Big Data

  Xin Gao, Program Chair, Computer Science

  Mar 19, 08:00 - Mar 21, 17:00

  B9 L2 H2

  We are now in the fourth paradigm of science: Data Science. The massive amount of structured and unstructured data has posed new challenges and opportunities to the fields of computer science and statistics. Traditional computational and statistical methods for data storage, curation, sharing, querying, updating, visualization, analysis, and privacy have been shown to fail in the big data scenario due to the unprecedented volume, velocity, variety, veracity and value of the big data. This conference will bring together a number of prominent researchers in Computer Science and Statistics with common interests and active research in big data, as well as the researchers at KAUST who regularly generate or face big data, such as those in bioscience and red sea research.

Feb 18 - Feb 24, 2018

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  Fifth KAUST-NVIDIA Workshop on Accelerating Scientific Applications Using GPUs

  Timothy Lanfear , Brent Leback

  Feb 18, 08:00 - Feb 20, 17:00

  B4 B5 A0215

  supercomputing

  The KAUST Supercomputing Laboratory is co-organizing with NVIDIA, a leader in accelerated computing and artificial intelligence, a full-day workshop on accelerating scientific applications using GPUs on Tuesday, February 20th, 2018 in the auditorium between buildings 4 and 5.

Dec 3 - Dec 9, 2017

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  Big Data Analyses in Evolutionary Biology

  Dec 4, 08:00 - Dec 6, 17:00

  B9 H2

  big data Big data analysis evolutionary biology

  This event is organized by CBRC with financial support from the KAUST Office of Sponsored Research

May 21 - May 27, 2017

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  PCCFD - Predictive Complex Computational Fluid Dynamics

  David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

  May 22, 08:45 - May 24, 05:00

  B9 L2 H1

  ​CFD algorithms applied mathematics numerical analysis Computer science

  The PCCFD workshop will focus on cutting-edge research in the field of algorithmic development for CFD and multi-scale complex flow simulations.

May 22 - May 28, 2016

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  Welcome at Kaust UQ School On Numerical Methods For Direct And Inverse Problems 2016 22-28 May

  Raul Tempone, Professor, Applied Mathematics and Computational Sciences

  May 22, 12:00 - May 28, 12:00

  B9 H1 R2322

  numerical methods stochastic differential equations statistics

  KAUST UQ SCHOOL 2016 is an annual base thematic conference at King Abdullah University of Science and Technology held by Raul Tempone, Professor of Applied Mathematics and Computational Sciences at CEMSE (Computer, Electrical and Mathematical Sciences & Engineering Division). Tempone’s interests in the mathematical foundation of computational science and engineering are reflected in this summer school. The school’s goal is to provide participants with an overview on the most recent research progress in the field of uncertainty quantification, with emphasis to • Multi-Level and Multi-Index

Mar 3 - Mar 9, 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.

Apr 22 - May 5, 2012

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  Scalable Hierarchical Algorithms for eXtreme Computing Workshop

  David Keyes, Senior Associate to the President, King Abdullah University of Science and Technology

  Apr 28, 08:00 - Apr 30, 16:00

  KAUST

  scientific computing

  The 2012 SHAX-C workshop focuses international expert attention on the prospects for the three great hierarchical algorithms of scientific computing: multigrid, fast transforms, and fast multipole methods. These methods are kernels in simulations based on formulations of partial differential equations, integral equations, and interacting particles – in short, they are scientific and engineering workhorses.