Events

Apr 3 - Apr 9, 2022

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  Phasewise-Conservative and Bound-Preserving Algorithms for Two-Phase Flow in Porous Media

  Shuyu Sun, Professor, Earth Science and Engineering

  Apr 7, 12:00 - 13:00

  B9 L2 H2

  Two or multiple phases in fluid mixture commonly occur in petroleum industry, where oil, gas and water are often produced and transported together. Petroleum reservoir engineers spent great efforts in drainage problems arising from the development and production of oil and gas reservoirs so as to obtain a high economic recovery, by developing, conducting, and interpolating the simulation of subsurface flows of reservoir fluids, including water, hydrocarbon, CO2, H2S for example in porous geological formation.
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  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini - Part 1

  Prof. Elisabetta Carlini, Department of Mathematics, University Of Sapienza, Italy

  Apr 7, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by and in order to model dynamic games with a large number of indistinguishable small players. In the model proposed in , the asymptotic equilibrium is described by means of a system of two Partial Diferential Equations (PDEs).
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  Approximation of Mean Field Games and related problems - Prof. Francisco Silva - Part 1

  Prof. Francisco Silva, Department of Mathematics, University Of Limoges

  Apr 7, 10:00 - 12:00

  B1 L4 R4102

  The aim of this course is to introduce some numerical methods to solve mean field games and related problems. Mean Field Games (MFGs) systems were introduced independently by and in order to model dynamic games with a large number of indistinguishable small players.
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  Finite element approximation of Stokes equations with non-smooth data

  Lucia Gastaldi, Professor Department of Mathematics, University of Brescia

  Apr 6, 16:00 - 17:00

  B1 L4 R4102

  Finite element methods

  Finite element approximation of Stokes equations with non-smooth data

Mar 27 - Apr 2, 2022

• 

  ProxSkip: Yes! Local Gradient Steps Provably Lead to Communication Acceleration! Finally!

  Peter Richtarik, Professor, Computer Science

  Mar 31, 12:00 - 13:00

  B9 L2 H2

  We introduce ProxSkip a surprisingly simple and provably efficient method for minimizing the sum of a smooth (ƒ) and an expensive nonsmooth proximable (ψ) function.
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  Random Persistence Diagram Generation

  Prof. Vasileios Maroulas, Director of AI and Data Science at the National Institute for Mathematical and Biological Synthesis (NIMBioS), University of Tennessee Knoxville.

  Mar 30, 15:00 - 16:00

  KAUST

  Topological data analysis (TDA) studies the shape patterns of data. Persistent homology (PH) is a widely used method in TDA that summarizes homological features of data at multiple scales and stores them in persistence diagrams (PDs). In this talk we will discuss a random persistence diagram generation (RPDG) method that generates a sequence of random PDs from the ones produced by the data. RPDG is underpinned by (i) a model based on pairwise interacting point processes for inference of persistence diagrams, and (ii) by a reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithm for generating samples of PDs. An example on a materials science problem will demonstrate the applicability of the RPDG method.
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  Monolithic matrix-free solver for fluid-structure interaction problems

  Michał Wichrowski, IWR, University of Heidelberg (Germany)

  Mar 28, 16:00 - 17:00

  B1 L4 R4214

  monolithic

  Monolithic matrix-free solver for fluid-structure interaction problems.
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  Research conference "Flow Simulation at the Exascale: Opportunities, challenges and its application in the industry"

  Matteo Parsani, David Keyes, Rasha Al Jahdali, Lisandro Dalcin, Bilel Hadri, Hong Im, Ravi Samtaney, Gabriel Wittum

  Mar 28, 08:30 - Mar 30, 17:00

  KAUST Library

  We are excited to announce the KAUST Research Conference on Flow simulation at the exascale: Opportunities, challenges, and its application in the industry, which will be held on March 28 – March 30, 2022 (#ExaCFDKAUST). The conference aims to bring together experts in flow simulation, computational mathematics, and high-performance computing. The goal is to define a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing and beyond. The conference will give space for presentations and discussions of computational fluid dynamics. As part of this event, we are accepting poster abstract submissions. The poster should present high-quality research contributions describing original and unpublished results of conceptual, constructive, empirical, experimental, or theoretical work in all areas of Computational Fluid Dynamics. For more information please visit the conference website.

Mar 20 - Mar 26, 2022

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  Regression models for count and proportion data

  Alexandre de Bustamante Simas , Research Scientist, Statistics

  Mar 24, 12:00 - 13:00

  B9 L2 H2

  Abstract In this talk we will present two classes of regression models. The first class is focused on overdispersed count models and is given by assuming that the response variable is obtained by mixing the Poisson distribution with an absolutely continuous distribution in the exponential family. The second class is focused on proportion data and assumes that the response variable follows a distribution given by the fraction $X/(X+Y)$, where $X$ and $Y$ are independent absolutely continuous random variables from the exponential family. Usually, the marginal likelihood of the response variables
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  Waste-free Sequential Monte Carlo

  Prof. Nicolas Chopin, Professor of Statistics, ENSAE, Paris

  Mar 23, 16:00 - 17:00

  KAUST

  A standard way to move particles in a SMC sampler is to apply several steps of a MCMC (Markov chain Monte Carlo) kernel. Unfortunately, it is not clear how many steps need to be performed for optimal performance. In addition, the output of the intermediate steps are discarded and thus wasted somehow. We propose a new, waste-free SMC algorithm which uses the outputs of all these intermediate MCMC steps as particles. We establish that its output is consistent and asymptotically normal. We use the expression of the asymptotic variance to develop various insights on how to implement the algorithm in practice. We develop in particular a method to estimate, from a single run of the algorithm, the asymptotic variance of any particle estimate. We show empirically, through a range of numerical examples, that waste-free SMC tends to outperform standard SMC samplers, and especially so in situations where the mixing of the considered MCMC kernels decrease across iterations (as in tempering or rare event problems).
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  The capabilities of summation-by-parts and structure-preserving operators for compressible computational fluid dynamics and reaction-diffusion models

  Mohammed Sayyari, Ph.D. Student, Applied Mathematics and Computational Sciences

  Mar 21, 14:00 - 16:00

  B2 L5 R5209

  With the algorithm's suitability for exploiting current petascale and next-generation exascale supercomputers, stable and structure-preserving properties are necessary to develop predictive computational tools. This dissertation uses the mimetic properties of SBP-SAT operators and the structure-preserving property of a new relaxation procedure for Runge--Kutta schemes to construct nonlinearly stable full discretizations for non-reactive compressible computational fluid dynamics (CFD) and reaction-diffusion models.
• 

  IVth KAUST MaS Workshop on Modelling and Simulation

  speakers from KAUST, Birmingham, Graz, Utrecht, Stuttgart, Frankfurt, Buffalo, Linz, Weissach, Lugano, Kaliningrad, Heidelberg, State College, Philadelphia, Torino, Riyadh

  Mar 21, 09:00 - Mar 23, 17:30

  B3 L5 R5209

  Applied Partial Differential Equations

  The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Models based on partial differential equations Novel models in biosciences Computational methods in empirical sciences. Numerical methods for partial differential equations Historical and scientific background Previous KAUST MaS workshops were held in 2017, 2018 and 2019 To download the Workshop's Agenda click here. Agenda Monday, 21 March 2022 Day 1 Saudi Arabia(AST) Speakers Description 10:00 Tony Chan (KAUST President) Opening Remarks 10:15 Gundolf Haase

Mar 13 - Mar 19, 2022

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  The Maxwell-Stefan system for multi-component diffusion

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Sciences

  Mar 17, 12:00 - 13:00

  B9 L2 H2

  The Maxwell-Stefan system is a system of equations commonly used to describe diffusion processes of multi-component systems. In this talk (i) I will describe modeling of multi-component systems, which leads to extensions of the Euler compressible dynamics system with mass and thermal diffusion. (ii) Will describe how the Maxwell-Stefan system emerges in the high-friction limit of multi-component Euler flows. (iii) Discuss some mathematical questions that this model raises and on the construction of numerical schemes for the Maxwell-Stefan system associated with the minimization of frictional dissipation.

Mar 6 - Mar 12, 2022

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  Some Variational Problem in Morphoelasticity

  Ulisse Stefanelli, Professor, Chair of Applied Mathematics and Modeling University of Vienna

  Mar 10, 17:00 - 18:00

  KAUST

  I will present some recent work in collaboration with Elisa Davoli (TU Wien) and Katerina Nik (University of Vienna) on a three-dimensional quasistatic morpholelastic model. The mechanical response of the body and its growth are modeled by the interplay of hyperelastic energy minimization and growth dynamics. An existence result is obtained by regularization and time-discretization, also taking advantage of an exponential-update scheme. Then, we allow the growth dynamics to depend on an additional scalar field describing a nutrient, and formulate an optimal control problem. Eventually, we tackle the existence of coupled morphoelastic and nutrient solutions, when the latter is allowed to diffuse and interact with the growing body. The preprint is available as arXiv:2110.05566.
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  Machine learning assisted forward and inverse design modeling

  Waqas Waseem Ahmed, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Mar 10, 12:00 - 13:00

  KAUST

  In recent years, machine learning has proven to be efficient in solving various physical problems through data-driven approaches. For example, in wave physics, models based on analytical and numerical schemes employ intensive trial-and-error tuning of material (and geometrical) parameters for 'on demand' wave properties, which require deep understanding of the physics and are computationally expensive. As a result, it is desired to develop intelligent models that learn the bidirectional mapping between different physical quantities and automate technological device design. In this presentation, I will discuss novel generative models for forward and inverse predictions that outperform human performance. In particular, I will show how machine learning can be used to design broadband acoustic cloaks, unidirectional non-Hermitian structures, and for solving the inverse scattering problem of shape recognition.
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  The conditional particle filter - scalable MCMC for dynamic models

  Prof. Matti Vihola, Professor of Statistics, University of Jyväskylä, Finland

  Mar 9, 15:00 - 16:00

  KAUST

  This talk focuses on a 'particle MCMC' method known as the conditional particle filter (CPF), or the particle Gibbs. The CPF is a slight algorithmic variant of the original particle filter, but serves a different purpose: it defines an MCMC transition targeting the HMM smoothing distribution. The empirical evidence suggests that certain variants of the CPF mix well even in high dimensions (with long observation records). We review some theoretical insights that consolidate such empirical findings, and justify why the CPF is often efficient for HMM inference.

Feb 27 - Mar 5, 2022

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-03-03

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Mar 3, 12:00 - 13:00

  B9 L2 H2

  Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects. We developed extensions of dynamic programming which allow us (i) to describe the set of objects under consideration, (ii) to perform a multi-stage optimization of objects relative to different criteria, (iii) to count the number of optimal objects, (iv) to find the set of Pareto optimal points for the bi-criteria optimization problem, and (v) to study the relationships between two criteria. The considered applications include optimization of decision trees and decision rule systems as algorithms for problem-solving, as ways for knowledge representation, and as classifiers, optimization of element partition trees for rectangular meshes which are used in finite element methods for solving PDEs, and multi-stage optimization for such classic combinatorial optimization problems as matrix chain multiplication, binary search trees, global sequence alignment, and shortest paths.
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  Modelling waves with structure-preserving numerical discretizations

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Feb 27, 14:00 - 16:00

  B9 L2 H2

  Models of physical phenomena include important qualitative properties, and any useful approximate solution of the model must respect these properties. Such properties include conservation or dissipation of energy, as well as positivity of quantities like mass, probability, or concentration. Preservation of these properties in computationally affordable numerical solutions of complex physical models remains a major challenge today. I will describe some recent advances in numerical methods for general dynamical systems that enable preservation of system dynamics and of bounds on the state, in the context of high-order accurate and efficient discretizations. The power of these methods will be demonstrated through applications in the area of surface water waves.

Feb 20 - Feb 26, 2022

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  Combined analysis of spatially misaligned data using Gaussian fields and the stochastic partial differential equation approach

  Feb 24, 12:00 - 13:00

  B9 L2 H2

  Spatially misaligned data are becoming increasingly common due to advances in both data collection and management in a wide range of scientific disciplines including the epidemiological, ecological and environmental fields. Here, we present a Bayesian geostatistical model for fusion of data obtained at point and areal resolutions. The model assumes that underlying all observations there is a spatially continuous variable that can be modeled using a Gaussian random field process.
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  Multi-physics Simulation of Plasmonic Photoconductive Devices and Antennas

  Hakan Bagci, Associate Dean for Students, Computer, Electrical and Mathematical Sciences and Engineering

  Feb 20, 12:00 - 13:00

  B9 L2 R2322 H1

  Multiphysics Simulation

  Terahertz (THz) frequency electromagnetic fields have numerous applications ranging from wireless communications to imaging systems and nondestructive testing, to material characterization. One of the main obstacles in the way of widespread industrial use of THz technologies is the difficulty of implementing compact and frequency-stable THz sources that can operate at room temperatures. Among a variety of possible options, photoconductive devices (PCDs) satisfy these conditions. Indeed, they have become one of the most promising candidates for THz source generation since recent advances in fabrication techniques, such as metasurface integration and nanostructured surface inclusions have significantly increased their optical-to-THz conversion efficiency and made them polarization insensitive.

Feb 13 - Feb 19, 2022

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  Modelling and Simulation of Biological Systems - 2022-02-17

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Feb 17, 12:00 - 13:00

  B9 L2 H2

  Biological systems are distinguished by their enormous complexity and variability. That is why mathematical modelling and computational simulation of those systems is very difficult, in particular thinking of detailed models which are based on first principles. The difficulties start with geometric modelling which needs to extract basic structures from highly complex and variable phenotypes, on the other hand also has to take the statistic variability into account.
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  Intermediate Distributions and Complexity Bounds for SMC

  Prof. Anthony Lee, School of Mathematics, University of Bristol

  Feb 16, 15:00 - 16:00

  KAUST

  It is now fairly common to use Sequential Monte Carlo (SMC) algorithms for normalizing constant estimation of high-dimensional, complex distributions without any particular structure. In order for the algorithm to give reasonable accuracy, it is well known empirically that one must introduce appropriate intermediate distributions that allow the particle system to “gradually evolve” from a simple initial distribution to the complex target distribution, and one must also specify an appropriate number of particles to control the error. Since both the number of intermediate distributions and the number of particles affect the computational cost of the algorithm, it is crucial to study and attempt to minimize the computational cost of the algorithm subject to constraints on the error.

Feb 6 - Feb 12, 2022

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  Exploring Spectral Dependence in Multivariate Time Series

  Hernando Ombao, Professor, Statistics

  Feb 10, 12:00 - 13:00

  KAUST

  Advances in imaging technology have given neuroscientists unprecedented access to examine various facets of how the brain “works”. Brain activity is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization (from neurons to regions of interest; and from transient events to long-term temporal dynamics). Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity. This is potentially interesting for brain scientists because functional brain networks are associated with cognitive function and mental and neurological diseases.
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  Phase Separation in Heterogeneous Media

  Irene Fonseca, Center for Nonlinear Analysis, Carnegie Mellon University

  Feb 8, 19:00 - 20:00

  KAUST

  A variational model in the context of the gradient theory for fluid-fluid phase transitions with small scale heterogeneities is studied. In the case where the scale of the small heterogeneities is of the same order of the scale governing the phase transition, the interaction between homogenization and the phase transitions process leads to an anisotropic interfacial energy. Bounds on the homogenized surface tension are established. In addition, a characterization of the large-scale limiting behavior of viscosity solutions to non-degenerate and periodic Eikonal equations in half-spaces is given. This is joint work with Riccardo Cristoferi (Radboud University, The Netherlands), Adrian Hagerty (USA), Cristina Popovici (USA), Rustum Choksi (McGill, Canada), Jessica Lin (McGill, Canada), and Raghavendra Venkatraman (NYU, USA).
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  Numerical analysis, physics, and high-performance computing: A firm three-hand shake for next-generation predictive computational fluid dynamics frameworks.

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Sciences

  Feb 6, 14:00 - 16:00

  B4 L5 R5220

  Together with the algorithm suitability to exploit current petascale and next-generation exascale supercomputers, robust, accurate, and structure-preserving discretizations are necessary for developing predictive computational tools. The research carried out in the Advanced Numerical Algorithms and Numerical Simulations Laboratory (AANSLab) leverages a multidisciplinary platform that integrates numerical analysis, physics, and high-performance computing. In particular, we focus on the analysis and development of novel numerical methods for ordinary and partial differential equations with provable properties such as nonlinear stability and conservation, and structure-preserving techniques. These properties are critical for designing reliable, efficient, and self-adaptive solvers for complex geometries – an essential cornerstone for next-generation computational frameworks. Current classes of partial differential equations that we are working on are the compressible Navier–Stokes equations, the Eulerian model for compressible heat-conducting flows, and the diffusion-reaction and convection-diffusion-reaction equations for molecular communication. We also use deep learning to complement and speed up the process of solving efficiently large-scale PDE-based problems. In this talk, I will summarize the progress we made in the last five years in the following areas: - Numerical analysis and algorithm development for robust, smart compressible flow solvers. - Development from the ground up of a new scalable hp-adaptive computational fluid dynamics (CFD) framework that places KAUST a few years ahead of the NASA CFD 2030 vision: o Applications and impact in the automotive and aerospace industry. o Improving knowledge of flow physics: Examples in detonation and aeroacoustics. - Advection-reaction-diffusion algorithms for molecular communication. Finally, I'll discuss our translational work to solve industrially relevant flow problems in partnership with Boeing, NASA Langley Research Center, and McLaren F1 Racing Team, and my future research plans.

Jan 30 - Feb 5, 2022

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  A Non-stationary Autologistic Model for Space-time Data

  Yiping Hong, Postdoctoral Research Fellow, Statistics

  Feb 3, 12:00 - 13:00

  KAUST

  Abstract In many research fields such as meteorology, ecology, and epidemiology, the spatio-temporal datasets are binary, describing the existence of particular species or events. The spatial dependence of the binary observations often exhibits nonstationarity. However, most nonstationary models in the literature are based on Gaussian random fields, which may be computational demanding. We propose a nonstationary spatio-temporal autologistic regression model, which allows the spatial covariances to vary in space. We investigate the spatial and temporal correlation of autologistic models with
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  Exoplanet Imaging Coronagraphic Mask Development and Testing: A North American Perspective

  Benjamin L. Gerard, Postdoctoral Scholar, University of California, UC Observatories

  Feb 2, 16:30 - 17:30

  KAUST

  Over 4000 exoplanets - planets beyond the Solar System - have been discovered since the first Nobel prize-winning exoplanet detection around a Sun-like star in 1995. The majority of these exoplanets have been detected by indirect methods, inferring the presence of the exoplanet by observing the star.
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  Elliptic PDEs: Regularity under minimal assumptions - Part 3

  Prof. Giuseppe Di Fazio, Department of Mathematics, University Of Catania, Italy

  Feb 2, 15:00 - 17:00

  B1 L4 R4102

  Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in proper way if one want to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.
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  Elliptic PDEs: Regularity under minimal assumptions - Part 2

  Prof. Giuseppe Di Fazio, Department of Mathematics, University Of Catania, Italy

  Jan 31, 15:00 - 17:00

  B1 L4 R4214

  Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in proper way if one want to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.

Jan 23 - Jan 29, 2022

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  Designing a Stable Periodic Orbit for a Triple Pendulum on a Cart

  Prof. Johann Reger, Automation and Systems Engineering, Technische Universität Ilmenau

  Jan 27, 12:00 - 13:00

  B1 L4 R4214

  In this talk an inversion-based control approach is presented for the generation and stabilization of a periodic orbit in a multi-link triple pendulum on a cart. To this end, a nominal trajectory is generated by formulating the posture transition problem as a two-point Boundary Value Problem (BVP) in an input-output representation. For solvability of the BVP, a setup function is introduced such that additional parameters are provided in the differential equation of the internal dynamics. Based on the linearized dynamics about the nominal trajectory, a linear-quadratic-Gaussian controller is implemented to compensate for measurement noise, model uncertainties, and external disturbances. This way we force a triple pendulum to move along a non-trivial periodic orbit and render it attractive. The high performance and accuracy of our approach is illustrated on an experimental setup.
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  Backward induction: from theory to empirics

  Konrad Grabiszewski, Instructional Professor, Applied Mathematics and Computational Sciences

  Jan 27, 12:00 - 13:00

  KAUST

  Backward induction, the cornerstone of dynamic game theory, is the classical algorithm applied to solve finite dynamic games with perfect and complete information. While theoretically sound and beautiful in its simplicity, backward induction does not perform so well when it comes to predicting human behavior. The objective of this seminar is twofold. First, we will understand what backward induction is and how to apply it on game-theoretic trees. Second, we will answer the question of whether backward induction is a good model of how people make choices in dynamic interactions.
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  Elliptic PDEs: Regularity under minimal assumptions - Part 1

  Prof. Giuseppe Di Fazio, Department of Mathematics, University Of Catania, Italy

  Jan 26, 15:00 - 17:00

  B1 L4 R4102

  Elliptic PDEs are ubiquitous both in Mathematics and in applications of Mathematics. Regularity of generalized solutions is a fundamental issue necessary to handle in a proper way if one wants to obtain qualitative information about solutions. My goal is to introduce the audience to the topic of regularity for elliptic PDEs under assumptions on the coefficients that are of minimal requirements.

Jan 9 - Jan 15, 2022

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  Multilevel Methods for Stochastic Forward and Inverse Problems

  Marco Ballesio, Ph.D., Applied Mathematics and Computational Sciences

  Jan 12, 11:00 - 12:00

  KAUST

  This thesis studies novel and efficient computational sampling methods for applications in three types of stochastic inversion problems: seismic waveform inversion, filtering problems, and static parameter estimation.

Dec 5 - Dec 11, 2021

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  Backward Error Analysis for Nonlinear Differential Equations

  David Ketcheson, Professor, Applied Mathematics and Computational Sciences

  Dec 9, 12:00 - 13:00

  KAUST

  Backward error analysis is a tool that allows us to understand the effect of errors without knowing exact solutions.

Nov 28 - Dec 4, 2021

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  Particle filter in high dimensions

  Dec 2, 12:00 - 13:00

  KAUST

  Filtering (or data assimilation) is the process of combining noisy observational data with an output of a numerical model to produce an optimal estimate of the evolving state of the system.

Nov 21 - Nov 27, 2021

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  An efficient high-order reconstructions multidimensional scheme with application to instabilities in direct initiation of gaseous detonations in free space

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Sciences

  Nov 25, 12:00 - 13:00

  KAUST

  When constructing high-order schemes for solving hyperbolic conservation laws with multi-dimensional finite volume schemes, the corresponding high-order reconstructions are commonly performed in characteristic spaces to eliminate spurious oscillations as much as possible.
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  Is probability theory the only viable solution to modelling statistical information?

  Prof. Jeremie Houssineau, Department of Statistics, University of Warwick.

  Nov 24, 15:00 - 16:00

  KAUST

  probability theory data assimilation

  Many alternatives to the probabilistic modelling of information have been proposed since the birth of modern Statistics; yet, few have been successfully applied to the complex inference problems that modern Statisticians are faced with.

Nov 14 - Nov 20, 2021

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  Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks

  Huang Huang, Ph.D., Statistics

  Nov 18, 12:00 - 13:00

  KAUST

  Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid.
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  Double Enriched Finite Volume Spaces for the DNS of fluid-Particle Interaction - Scientific Conference

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  Nov 17, 14:00 - 14:40

  KAUST

  Plenary Sessions

  Abstract We present double enriched finite volume spaces for the simulation of free particles in a fluid. This involves forces being exchanged between the particles and the fluid at the interface. In an earlier work we derived a monolithic scheme which includes the interaction forces into the Navier-Stokes equations by direct coupling. In multiphase flows oscillations and spurious velocities are a common issue. The surface force term yields a jump in the pressure and therefore the oscillations are usually resolved by extending the spaces on cut elements in order to resolve the discontinuity

Nov 7 - Nov 13, 2021

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  Compatibility conditions for finite element approximations

  Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering

  Nov 11, 12:00 - 13:00

  KAUST

  In the classical theory of the finite element approximation of elliptic partial differential equations, based on standard Galerkin schemes, the energy norm of the error decays with the same rate of convergence as the best finite element approximation, without any additional requirements on the involved spaces.
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  Manifold Markov Chain Monte Carlo Methods for Bayesian Inference In Diffusion Models

  Prof. Alexandros Beskos, the Dept of Statistical Science, University College London (UCL)

  Nov 10, 15:00 - 16:00

  KAUST

  markov chains bayesian inference nonlinearity

  Bayesian inference for nonlinear diffusions, observed at discrete times,is a challenging task that has prompted the development of a number of algorithms, mainly within the computational statistics community. We propose a new direction, and accompanying methodology - borrowing ideas from statistical physics and computational chemistry - for inferring the posterior distribution of latent diffusion paths and model parameters, given observations of the process
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  On Recently Developed Non-Gaussian Priors and Sampling Methods with Application to Industrial Tomography

  Prof. Lassi Roininen, Applied Mathematics, LUT University

  Nov 9, 15:00 - 16:00

  B1 L4 R4214

  Non-Gaussian spatio-temporal processes Bayesian Inversion machine learning

  We consider two sets of new priors for Bayesian inversion and machine learning: The first one is based on mixture of experts models with Gaussian processes. The target is to estimate the number of experts and their parameters, and to make state estimation. For sampling, we use SMC^2. For non-Gaussian priors, we discuss Cauchy priors and the generalisation to high-order Cauchy fields and further generalisation to alpha-stable fields. For sampling, we use a selection of modern MCMC tools. Finally, we apply some of the methods and models to an industrial tomography problem on estimating log internal structure, measured at sawmills, based on X-ray, RGB camera and laser scanning.
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  Computational Challenges in Sampling and Representation of Uncertain Reaction Kinetics in Large Dimensions

  Saja Almohammadi, Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 8, 16:00 - 19:00

  B3 L5 R5220

  Constructing functional representations of the key quantities of interest (QoIs), the ignition delay time ( ign), of an uncertain ignition reaction in high dimension is our main goal. First, attention is focused on the ignition delay time of an iso-octane air mixture, using a detailed chemical mechanism with 3,811 elementary reactions. Uncertainty in all reaction rates is directly accounted for using associated uncertainty factors, assuming independent log uniform priors. A Latin hypercube sample (LHS) of the ignition delay times was first generated, and the resulting database was then exploited to assess the possibility of constructing polynomial chaos (PC) representations in terms of the canonical random variables parameterizing the uncertain rates.

Oct 31 - Nov 6, 2021

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  Joint modeling of dependent diverse format data: A review from several case-studies

  Giovani Silva, PhD in Mathematics (IST) at the University of Lisbon

  Nov 4, 12:00 - 13:00

  KAUST

  his talk reviews some fundamental and practical issues related to the formulation and analysis of joint models of mixed types of outcomes with latent variables, with particular emphasis on both several case-studies in applied statistics and their computational implementation
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  Particle Approximations For Partially Observed Fluid Dynamics Models

  Professor Dan Crisan, Mathematics, Imperial College London

  Nov 3, 15:00 - 16:00

  KAUST

  fluid dynamics data assimilation stochastic

  Fluid dynamics models are ubiquitous in a multitude of applications. One of the most important applications of fluid dynamics models is numerical weather prediction. Modern numerical weather prediction combines sophisticated nonlinear fluid dynamics models with increasingly accurate high-dimensional data. This process is called data assimilation and it is performed every day at all major operational weather centers across the world. Data assimilation (DA) requires massive computing capabilities as realistic atmosphere-ocean models typically have billions of degrees of freedom. I will give a short overview of the ongoing research that aims to drastically decrease the required DA computational effort by reducing the dimension of the models involved and using stochastic perturbations to account for the unresolved scales. The incorporation of observation data is done by using particle approximations suitably adapted to solve high-dimensional problems.
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  Control and Estimation for Partial Differential Equations and Extension to Fractional Systems

  Lilia Ghaffour , Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 1, 15:00 - 17:00

  KAUST

  Partial differential equations (PDEs) are used to describe multi-dimensional physical phenomena. However, some of these phenomena are described by a more general class of systems called fractional systems (FS).
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  High order schemes for hyperbolic balance laws - Topic 3

  Giovanni Russo, Full Professor, Mathematics and Computer Science, University of Catania, Italy

  Nov 1, 09:00 - 10:00

  B1 L4 R4102

  Semi-implicit schemes for evolutionary partial differential equations. Topic 3 - construction of more general schemes for evolutionary partial differential equations, in which the stiffness may be of a different type than the one previously considered. Several examples will be given illustrating the general procedure.

Oct 24 - Oct 30, 2021

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  Recent progress on symbolic computation for PDE and their numerical schemes

  Diogo Gomes, Program Chair, Applied Mathematics and Computational Sciences

  Oct 28, 12:00 - 13:00

  KAUST

  The qualitative study of PDEs often relies on integral identities and inequalities. For example, for time-dependent PDEs, conserved integral quantities or quantities that are dissipated play an important role. In particular, if these integral quantities have a definite sign, they are of great interest as they may provide control on the solutions to establish well-posedness.
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  High order schemes for hyperbolic balance laws - Topic 2

  Giovanni Russo,Full Professor,Mathematics and Computer Science, University of Catania, Italy

  Oct 28, 09:00 - 10:00

  B1 L4 R4102

  Implicit-Explicit schemes for hyperbolic systems with stiff relaxation. Topic 2 - hyperbolic relaxation models and to the methods for their numerical solution. After introduction of hyperbolic-hyperbolic and hyperbolic-parabolic type relaxation problem, conservative finite difference space discretization will be introduced.
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  High order schemes for hyperbolic balance laws - Topic 1

  Giovanni Russo, Professor, Mathematics and Computer Science, University of Catania, Italy

  Oct 27, 09:00 - 10:00

  B1 L4 R4102

  Construction of high order finite volume and finite difference shock-capturing schemes for conservation laws. Topic 1 - illustrating how to construct shock capturing schemes for conservation laws. We focus on semi-discrete schemes based on the method of lines.