Events

Oct 8 - Oct 2, 2022

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  Geometry and Computational Design of Equilibrium Structures

  Oct 4, 15:30 - 17:00

  B1 L3 R3119

  Geometry computational design

  Freeform structures play a prominent role in contemporary architecture. In order to stay within reasonable cost limits, computational shape design has to incorporate aspects of structural analysis and fabrication constraints. The talk discusses solutions to important problems in this area. They concern the design of polyhedral surfaces with nearly rectangular faces, polyhedral surfaces in static equilibrium, the smoothest visual appearance of polyhedral surfaces and the closely related problem of finding material-minimizing forms and structures. From a methodology perspective, there is an interplay of geometry, mechanics and optimization. Classical subjects such as isotropic geometry, a simple Cayley-Klein geometry, play a role as well as most recent developments in discrete differential geometry. We also show how practical requirements have led to new results and open problems in geometry.
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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  Oct 4, 12:00 - 13:00

  B9 L2 R2322

  Dynamic programming combinatorial optimization data mining

  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.
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  A new class of random field models for data on networks

  David Bolin, Professor, Statistics

  Oct 3, 12:00 - 13:00

  B9 L2 R2322 H1

  applied statistics machine learning Gaussian processes

  Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Oct 1 - Sep 25, 2022

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  Modeling and Numerics of Sorption Kinetics

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

  Sep 27, 15:30 - 17:00

  B1 L3 R3119

  modeling sorption kinetics

  An efficient method is proposed for the numerical solution of the Stokes equations in a domain with a moving bubble and two techniques for the treatment of the boundary conditions are adopted and then compared. The treatment of diffusion of surfactants (anions and cations) in presence of an oscillating bubble is an interesting interdisciplinary problem, with applications to chemistry and biology.
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  Deep Learning and Scientific Computing

  Jinchao Xu, Professor, Applied Mathematics and Computational Science

  Sep 27, 12:00 - 13:00

  B9 L2 R2322

  Deep learning scientific computing

  In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Sep 24 - Sep 18, 2022

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  Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting

  Daniel Paulin, Assistant Professor, School of Mathematics, University of Edinburgh.

  Sep 20, 15:30 - 17:00

  B1 L3 R3119

  Monte Carlo MCMC

  In this paper, we propose a detailed theoretical study of one of these algorithms known as the split Gibbs sampler. Under regularity conditions, we establish explicit convergence rates for this scheme using Ricci curvature and coupling ideas. We support our theory with numerical illustrations.
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  Waves Functional Materials: From theory to applications

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Sep 20, 12:00 - 13:00

  B9 L2 R2322

  waves Wave functional materials Applications

  No abstract is available.

Sep 17 - Sep 11, 2022

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  On the resolution of a theoretical question related to the nature of local training in federated learning

  Peter Richtarik, Professor, Computer Science

  Sep 13, 15:30 - 17:00

  B1 L3 R3119

  machine learning mathematical optimization communications algorithms

  In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.
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  Solvers for Coupled Problems in Porous Media Science

  Arne Naegel

  Sep 13, 12:00 - 13:00

  B9 L2 R2322

  applied statistics

  No abstract is available.

Sep 10 - Sep 4, 2022

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  Pore-Scale Two-Phase Flows: Modeling, Algorithms, and Applications

  Sep 6, 15:30 - 17:00

  B1 L3 R3119

  Multiphase flows

  Abstract 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. Field-scale or Darcy-scale simulation has conventionally and routinely used
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  Low Rank Everywhere (Almost)

  David Keyes, Professor, Applied Mathematics and Computational Science

  Sep 6, 12:00 - 13:00

  B9 L2 R2322

  computational science low rank hierarchical low rank data sparsity

  Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.

Sep 3 - Aug 28, 2022

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  Emergence of transportation networks as a self-regulated process

  Jan Haskovec, Senior Research Scientist, Applied Mathematics and Computational Science

  Aug 30, 15:30 - 17:00

  B1 L3 R3119

  random process transportation networks

  In this talk, we shall explain how transportation networks emerge as a self-regulating process, with a particular focus on applications in biology (leaf venation in plants, neuronal networks in animals). We start by introducing a purely diffusive model with tensor-valued diffusivity, derived as a gradient flow of a broad class of entropy dissipations. The introduction of a prescribed electric potential leads to the Fokker-Planck equation. We show that with quadratic entropy density modeling Joule heating, the model is convex with respect to the diffusivity tensor.
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  Introduction to AMCS / STAT Graduate Seminar 398

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Aug 30, 12:00 - 13:00

  B9 L2 R2322

  In this section, prof. Gabriel will be introducing the graduate seminar and its requirements.

Jul 9 - Jul 3, 2022

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  Mean-Field Games on Networks and Wardrop Equilibria - PhD Dissertation Defense

  Fatimah H. Al Saleh, PhD Student, Applied Mathematics and Computational Sciences, KAUST, Saudi Arabia

  Jul 6, 10:00 - 12:00

  KAUST

  This thesis consists of three main parts. In the first part, we discuss first-order stationary mean-field games (MFGs) on networks. In the second part, we discuss the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary MFGs. First, we build the Wardrop model on networks. Second, we show how to convert the MFG model into a Wardrop model. Next, we recover the MFG solution from the Wardrop solution. Finally, we study the calibration of MFGs with Wardrop travel cost problems. In the third part, we explain the algorithm for solving the algebraic system associated with the MFG numerically, then, we present some examples and numerical results.

Jun 4 - May 29, 2022

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  Stochastic differential games, network of networks and the crowd evacuation problem

  Dr. Julian Barreiro Gomez, Center on Stability, Instability, and Turbulence (SITE) at the New York University in Abu Dhabi

  Jun 1, 16:00 - 17:00

  B1 L4 R4102

  In this talk, we present a class of stochastic differential games that can incorporate the distribution of the variables of interest (e.g., the system states and/or decision-makers' actions) into the strategic-interaction problem. We motivate the use of this type of differential games in networked large-scale applications that cover a high variety of engineering systems. In particular, we focus on the crowd evacuation problem. First, we only consider local aggregated congestion terms that penalize the magnitude of the decision-makers’ strategies allowing us to avoid the formation of congestion. Second, we consider both local and global aggregated congestion terms to perform crowd aversion during the evacuation procedure. We present some numerical results and few future directions, e.g., the case where decision-makers do not have prior knowledge about the geometry of the structure to be evacuated neither the existing obstacles.
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  Network processing in the NIC: the case for specialized hardware architectures

  Prof. Giuseppe Bianchi, University of Roma Tor Vergata, Italy

  May 29, 12:00 - 13:00

  B9 Hall R2325

  Network specialized hardware architectures

  In the last decade, the networking research community has significantly fueled the network softwarization and virtualization trend. Network processing tasks, originally performed by dedicated hardware appliances, were converted into software components running on commodity hardware, and deployed in relevant cloud infrastructures (central and/or edge).

May 28 - May 22, 2022

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  Mathematical Problems in Deep Learning

  Prof. Hailiang Liu, Department of Mathematics, Iowa State University, USA

  May 25, 15:00 - 16:00

  B1 L4 R4102

  I shall present some mathematical problems encountered in deep learning models. The results include optimal control of selection dynamics for deep neural networks, and gradient methods adaptive with energy. Some of the computational questions that will be addressed have a more general interest in engineering and sciences.

May 21 - May 15, 2022

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  On the Precipice of a Global Transition Toward Electric Vehicles

  Peter Rawlinson, the Chief Executive Officer and Chief Technology Officer of Lucid

  May 19, 14:30 - 15:30

  B20 L2 R2011

  Global Transition Toward Electric Vehicles Revolutionary technology

  In this wide-ranging conversation with Peter Rawlinson, Lucid Motors’ CEO and CTO, he will discuss why he believes the world is on the precipice of a global transition toward electric vehicles, and how Lucid’s revolutionary technology and design will be at the forefront of one of the most significant transformations of our time.
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  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop

  Stochastic Numerics PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)

  May 15, 08:00 - May 28, 17:00

  KAUST Campus

  This scientific meeting will concentrate on stochastic algorithms and their rigorous numerical analysis for various problems, including statistical learning, optimization, and approximation. Stochastic algorithms are valuable tools when addressing challenging computational problems.

May 14 - May 8, 2022

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  Non-matching methods for the simulation of multi-dimensional and multi-physics coupled problems

  Prof. Luca Heltai, Applied Mathematics, International School for Advanced Studies

  May 12, 15:00 - 16:00

  B1 L4 R4102

  Real life multi-phase and multi-physics problems coupled across different scales present outstanding challenges, whose practical resolution often require unconventional numerical methods.
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  An Introduction to the Boltzmann equation - Part 3

  Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 12, 14:00 - 16:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
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  An Introduction to the Boltzmann equation - Part 2

  Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 11, 15:00 - 17:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
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  How to Use Mathematics to Predict Cancer Patient Responses to Immuno-therapy

  Prof. Helen Moore, MD-Pulmonary Systems Medicine Department, University of Florida, USA

  May 10, 17:00 - 18:00

  KAUST

  How to Use Mathematics to Predict Cancer Patient Responses to Immuno-therapy.
• 

  An Introduction to the Boltzmann equation - Part 1

  Prof. Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 10, 14:00 - 16:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.

Apr 30 - Apr 24, 2022

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  Geometry for computational design and fabrication

  Apr 28, 12:00 - 13:00

  B9 L2 H2

  Geometry plays an important role in the design and fabrication of so-called freeform shapes. This talk will illustrate the fruitful interplay between theory and applications in this area. The focus will be on discrete differential geometry and on applications in architecture and fabrication-aware design.
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  Variational Inference for Cutting Feedback in Misspecified Models

  Prof. David Nott, Department of Statistics and Data Science, National University of Singapore

  Apr 27, 10:00 - 11:00

  KAUST

  Bayesian analyses combine information represented by different terms in a joint Bayesian model. When one or more of the terms is misspecified, it can be helpful to restrict the use of information from suspect model components to modify posterior inference.
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  Applied Optimization: from Energy Systems to Trajectory Planning

  Ricardo Manuel Pinto de Lima, Research Scientist, Applied Mathematics and Computational Science

  Apr 24, 14:00 - 15:00

  B9 L2 H2

  This talk focus on advanced modeling and optimization techniques to solve complex problems in the scheduling and planning of processes, energy systems, and water systems. Modeling and optimization techniques integrated with other disciplines have a paramount role in supporting the energy transition planning and the water-energy nexus. Specifically, optimizing the integration of renewable energy sources and new energy carriers in the energy mix, increasing the efficiency of existing and novel systems (enterprise-wide, industry, buildings, supply chains), and supporting decarbonization in industry. First, we describe canonical optimization formulations and show their flexibility and capabilities to address complex optimization problems. Examples of applications include process synthesis, production scheduling, short-term hydro scheduling, unit commitment, and even the transfer of human resources within large organizations. Then we focus on applying stochastic optimization approaches to address a virtual power plant's self-scheduling and market involvement, and finally on applying advanced optimization strategies for trajectory planning of underwater vehicles in uncertain and transient flow fields.

Apr 23 - Apr 17, 2022

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  Enhancing mixing in binary fluids

  Prof. Peter J. Schmid, Professor, Mechanical Engineering, KAUST

  Apr 21, 12:00 - 13:00

  B9 L2 H2

  Mixing is a key process in a wide variety of technological applications. Pharmaceuticals, consumer products, and oil & gas derivatives, among many other products, rely on an energy-efficient, robust and effective mixing process. We will formulate the mixing process as a PDE-constrained optimization problem using special norms that concentrate on the mixedness of a passive scalar transported by nonlinear governing equations.

Apr 16 - Apr 10, 2022

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  Biomedical research: a world of statistical challenges

  Prof. David Gomez-Cabrero, Biological, Environmental Science and Eng, KAUST

  Apr 14, 12:00 - 13:00

  B9 L2 H2

  A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case-studies where statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.
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  Approximation of Mean Field Games and related problems - Prof. Francisco Silva -Part 3

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

  Apr 11, 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.
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  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini - Part 3

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

  Apr 11, 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.
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  Approximation of Mean Field Games and related problems Prof. Francisco Silva -Part 2

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

  Apr 10, 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.
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  Approximation of Mean Field Games and related problems - Prof. Elisabetta Carlini -Part 2

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

  Apr 10, 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.

Apr 9 - Apr 3, 2022

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

  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 [4] and [5] in order to model dynamic games with a large number of indistinguishable small players. In the model proposed in [5], 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 [4] and [5] 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

Apr 2 - Mar 27, 2022

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  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 26 - Mar 20, 2022

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  Regression models for count and proportion data

  Alexandre de Bustamante Simas , Senior 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 Science

  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.
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  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 19 - Mar 13, 2022

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  The Maxwell-Stefan system for multi-component diffusion

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Science

  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 12 - Mar 6, 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 Science

  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.

Mar 5 - Feb 27, 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 Science

  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.