Events

May 8 - May 15, 2022

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.
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 24 - May 1, 2022

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.
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.

Apr 17 - May 1, 2022

Applied Optimization: from Energy Systems to Trajectory Planning

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

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 17 - Apr 24, 2022

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 10 - Apr 17, 2022

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.
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.
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.

Apr 3 - Apr 17, 2022

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.
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 3 - Apr 10, 2022

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.
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.
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).
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 3, 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.
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.
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.
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 27, 2022

Mar 13 - Mar 20, 2022

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 13, 2022

Feb 27 - Mar 6, 2022

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.

Feb 20 - Mar 6, 2022

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 27, 2022

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.

Feb 13 - Feb 27, 2022

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 20, 2022

Feb 6 - Feb 13, 2022

Jan 30 - Feb 13, 2022

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 6, 2022

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
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.
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.
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 30, 2022

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.
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.
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 16, 2022

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 12, 2021

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 5, 2021

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 28, 2021

Nov 14 - Nov 21, 2021

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.