Events

Nov 14 - Nov 21, 2021

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 14, 2021

Oct 31 - Nov 7, 2021

Oct 24 - Oct 31, 2021

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.
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.
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.
On supercomputers and engineering applications: DNS, modeling, and GPUs

Mathis Bode, Researcher, Institute for Combustion Technology (ITV) at RWTH Aachen University

Oct 26, 14:00 - 15:00

B2 L5 R5209

Navier-Stokes supercomputers computational fluid dynamics

The numerical solution of multi-physics problems relying on the Navier-Stokes equations has kept multiple generations of supercomputers busy. For fundamental problems, computational fluid dynamic aims to resolve all relevant time and length scales, which is then known as direct numerical simulation (DNS).
Monotonicity method for Mean-Field games

Teruo Tada, Ph.D. Student, Applied Mathematics and Computational Sciences

Oct 25, 14:00 - 16:00

B1 R4214

During my Ph.D. program, we have studied mean-field games (MFGs). MFGs model games with large populations of rational agents. The agents search for their optimal strategies and trajectories to minimize an individual cost, which depends on the statistical distribution of the population. Although it is quite hard to consider the systems of large populations in the numerical analysis, we can expect to consider the average effect given by the populations because the influence of each agent should be small.

Oct 17 - Oct 31, 2021

Regularity for singular and degenerate pdes: qualitative vs. sharp estimates

José Miguel Urbano, Professor of Mathematics at the University of Coimbra, Portugal

Oct 24, 12:00 - 13:00

B9 L2 H2

Singular and degenerate partial differential equations are unavoidable in the modelling of several phenomena, from phase transitions to flows in porous media or chemotaxis. They encompass a crucial issue in the analysis of pdes, namely wether we can still derive analytical estimates when the crucial algebraic assumption of ellipticity collapses.

Oct 17 - Oct 24, 2021

Enabling Accurate and Efficient Simulation in Visual Computing - Graduate Seminar - AMCS - STAT

Dominik L. Michels, Associate Professor, Computer Science

Oct 21, 12:00 - 13:00

KAUST

The overarching goal of Prof. Michels' Computational Sciences Group within KAUST's Visual Computing Center is enabling accurate and efficient simulations for applications in Scientific and Visual Computing. Towards this goal, the group develops new principled computational methods based on solid theoretical foundations.

Oct 10 - Oct 17, 2021

Statistical methods to evaluate the effectiveness of cancer treatments in clinical trials

Denis Rustand, Postdoctoral Research Fellow, Statistics

Oct 14, 12:00 - 13:00

KAUST

Assessing the effectiveness of cancer treatments in clinical trials raises multiple methodological challenges that need to be properly addressed in order to produce a reliable estimate of treatment effects.
Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation

Nikolas Kantas, Associate Professor, Department of Mathematics, Imperial College London

Oct 13, 15:30 - 16:30

KAUST

parameter estimation stochastic differential equations stochastic

We consider the problem of parameter estimation for a McKean stochastic differential equation, and the associated system of weakly interacting particles. The problem is motivated by many applications in areas such as neuroscience, social sciences (opinion dynamics, cooperative behaviours), financial mathematics, statistical physics. We will first survey some model properties related to propagation of chaos and ergodicity and then move on to discuss the problem of parameter estimation both in offline and on-line settings. In the on-line case, we propose an online estimator, which evolves according to a continuous-time stochastic gradient descent algorithm on the asymptotic log-likelihood of the interacting particle system. The talk will present our convergence results and then show some numerical results for two examples, a linear mean field model and a stochastic opinion dynamics model. This is joint work with Louis Sharrock, Panos Parpas and Greg Pavliotis. Preprint: https://arxiv.org/abs/2106.13751
Theory and Application of Neural Networks III

Jinchao Xu, Affiliate Professor of Information Sciences and Technology, Penn State University

Oct 13, 09:00 - 10:00

B9 L2 R2322

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.
Theory and Application of Neural Networks II

Jinchao Xu, Affiliate Professor of Information Sciences and Technology, Penn State University

Oct 12, 09:00 - 10:00

B4 B5 A0215

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.
Close-form Solutions of A Dynamic Monetary Model

Jenny Xiaoe Li, Associate Professor, Economics and Mathematics, Penn State University, Pennsylvania

Oct 11, 14:00 - 15:00

B4 B5 A0215

This talk is devoted to the study of a monetary model proposed by Rotemberg (Journal of Political Economy, 1984). Rotemberg’s model provided a general dynamic structure for investigating government intervention in the open market operation and it has inspired the development of many other models in related fields. This talk concerns a very basic theoretical question on the model, namely the existence and the uniqueness of the equilibrium, which has been an open problem since the publication of Rotemberg’s original paper. This question was partially addressed by Rotemberg by analyzing the linearization of the equation which governs the equilibrium and obtaining a numerical solution around the steady state equilibrium.
Theory and Application of Neural Networks I

Jinchao Xu, Affiliate Professor of Information Sciences and Technology, Penn State University

Oct 11, 09:00 - 10:00

B4 B5 A0215

I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.

Oct 3 - Oct 10, 2021

Quantifying Uncertainty with a Derivative Tracking SDE Model and Application to Wind Power Forecast Data

Raul Tempone, Professor, Applied Mathematics and Computational Sciences

Oct 7, 12:00 - 13:00

KAUST

We develop a data-driven methodology based on parametric Itô's Stochastic Differential Equations (SDEs) to capture forecast errors' asymmetric dynamics, including the forecast's uncertainty at time zero.

Sep 26 - Oct 3, 2021

Discriminant analysis: Fundamental limits and Optimal designs

Abla Kammoun, Senior Research Scientist, Electrical and Computer Engineering

Sep 30, 12:00 - 13:00

KAUST

Despite the recent advances in big data processing, enabled by the emergence of large-scale machine learning techniques, several statistical questions regarding the behavior in the regime of high dimensions of well-established and fundamental methods have remained unresolved.
Randomized multilevel Monte Carlo for inference

Kody J.H. Law, Professor, Applied Mathematics in the Department of Mathematics, University of Manchester and Manchester Institut

Sep 29, 13:00 - 14:00

KAUST

Often in the context of data centric science and engineering applications, one endeavours to learn complex systems in order to make more informed predictions and high stakes decisions under uncertainty. Some key challenges which must be met in this context are robustness, generalizability, and interpretability.

Sep 12 - Sep 19, 2021

Developing Aramco’s Next Generation Operational Systems for the Red Sea and the Arabian Gulf

Ibrahim Hoteit, Professor, Earth Science and Engineering

Sep 16, 12:00 - 13:00

KAUST

The talk will present our efforts to develop the next generation operational systems for the Red Sea and the Arabian Gulf, as part of Aramco’s resolution toward the Fourth Industrial Revolution. These integrated systems, we refer to as iReds and iGulf, have been built around state-of-the-art ocean-atmosphere-wave general circulation models that have been specifically developed for the region and nested within the global weather systems.

Sep 5 - Sep 12, 2021

Aug 29 - Sep 5, 2021

Aug 15 - Aug 22, 2021

Machine Learning in Healthcare: When Low Sample Size is not a Limitation

Dr. Ricardo Henao, Biostatistics and Bioinformatics, Duke University

Aug 17, 14:30 - 15:30

KAUST

In this talk, I will describe three use cases that highlight present challenges and opportunities for the development of machine learning methodology for applications in healthcare. First, I will describe the development of simple word embedding approaches for bag of-documents classification and its applications to diagnosis of peripheral artery disease from clinical narratives. Second, I will present an approach for volumetric image classification that leverages attention mechanisms, contrastive learning and feature-encoding sharing for geographic atrophy prognosis from optical coherence tomography images. Third, I will discuss machine learning approaches for multi-modal and multi-dataset integration for biomarker discovery from molecular (omics) data. To conclude, I will summarize the contributions and insights in each of these different directions in which relatively low sample sizes are the common denominator.

Aug 8 - Aug 22, 2021

AMCS Workshop for Higher Education Instructors

Aug 15, 10:00 - Aug 26, 16:00

KAUST Campus

linear algebra Programming language

Workshop's Agenda View Workshop's Agenda. Topics for the event: Core sessions on Linear Algebra Core sessions on applications on emerging teaching methodologies Programming language (practical applications) Keynote lectures on emerging teaching methodologies and current research applications Core lab and research center visits and presentations Networking sessions and social activities with KAUST faculty, researchers, alumni, and students This workshop is for TAs and lecturers from in-Kingdom Universities. It is not open to KAUST students and the community. Applications are closed now!

Jun 13 - Jun 20, 2021

Jun 6 - Jun 13, 2021

An introduction to bifurcation theory and applications to MFG - Session 2

Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy

Jun 10, 14:00 - 17:00

KAUST

In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.
The envelope formula and its applications in variational analysis - Session 1

Adjunct Prof. Levon Nurbekyan, Department of Mathematics at UCLA

Jun 9, 19:00 - 21:00

KAUST

I will focus on the envelope formula, an essential tool and a unifying framework for the first-order analysis of value functions in parameter-dependent optimization problems. In particular, I will discuss formal differentiation rules of value functions and derive a few familiar examples as particular cases of this technique, such as the Hamilton-Jacobi-Bellman PDE and the adjoint method. I will then discuss how to turn these formal differentiation rules into rigorous theorems via perturbation analysis of optimization problems. Finally, I will apply these ideas to parameter identification problems based on optimal transportation distances and variational analysis of mean-field games.
An introduction to bifurcation theory and applications to MFG - Session 1

Marco Cirant, Assistant Professor, Mathematic Department, University of Padova, Italy

Jun 8, 15:00 - 18:00

KAUST

In this short course I will introduce some elements of bifurcation theory, such as the Lyapunov-Schmidt reduction, the bifurcation from the simple eigenvalue, and the Krasnoselski bifurcation theorem. Then, I will discuss some applications to the theory of MFG systems: existence of periodic in time solutions, and multi-population problems.

May 23 - May 30, 2021

Minimization problems involving polyconvex functionals and an H1-projection problem - Session 3

Wilfrid Gangbo, Professor of mathematics at the University of California, Los Angeles

May 25, 19:00 - 21:00

KAUST

We recall the state of the art and the role of polyconvexity in the calculus of variations. Then we keep our focus on a particular polyconvex function, applicable to the study of Euler incompressible fluids. We prove the existence, uniqueness, and regularity of minimizers of a polyconvex functional which corresponds to the H1-projection of measure-preserving maps. Our result introduces a new criteria on the uniqueness of the minimizer, based on the smallness of the Lagrange multiplier. No estimate on the second derivatives of the pressure is needed to get a unique global minimizer. We introduce a minimizing movement scheme to construct Lr-solutions of the Navier-Stokes equation (NSE) for a short time interval. Our scheme is an improved version of the split scheme introduced in Ebin–Marsden in 1970, and allows us to solve the equation with less regular initial data as opposed to more regular initial data requirement in the 1970 Ebin–Marsden’s work. (Most of the material of these lectures is based on a joint work with M. Jacobs and I. Kim).

May 9 - May 16, 2021

Minimization problems involving polyconvex functionals and an H1-projection problem - Session 2

Wilfrid Gangbo, Professor of mathematics at the University of California, Los Angeles

May 11, 19:00 - 21:00

KAUST

We recall the state of the art and the role of polyconvexity in the calculus of variations. Then we keep our focus on a particular polyconvex function, applicable to the study of Euler incompressible fluids. We prove the existence, uniqueness, and regularity of minimizers of a polyconvex functional which corresponds to the H1-projection of measure-preserving maps. Our result introduces a new criteria on the uniqueness of the minimizer, based on the smallness of the Lagrange multiplier. No estimate on the second derivatives of the pressure is needed to get a unique global minimizer. We introduce a minimizing movement scheme to construct Lr-solutions of the Navier-Stokes equation (NSE) for a short time interval. Our scheme is an improved version of the split scheme introduced in Ebin–Marsden in 1970, and allows us to solve the equation with less regular initial data as opposed to more regular initial data requirement in the 1970 Ebin–Marsden’s work. (Most of the material of these lectures is based on a joint work with M. Jacobs and I. Kim).

May 2 - May 9, 2021

Apr 25 - May 2, 2021

The Role of Volcanic Activity in Climate and Global Change

Georgiy L. Stenchikov, Professor, Earth Science and Engineering

Apr 29, 12:00 - 13:00

KAUST

Explosive volcanic eruptions are magnificent events that in many ways affect the Earth’s natural processes and climate. They cause sporadic perturbations of the planet’s energy balance, activating complex climate feedbacks and providing unique opportunities to better quantify those processes. We know that explosive eruptions cause cooling in the atmosphere for a few years, but we have just recently realized that they affect the major climate variability modes and volcanic signals can be seen in the subsurface ocean for decades. The volcanic forcing of the previous two centuries offsets the ocean heat uptake and diminishes global warming by about 30%. In the future, explosive volcanism could slightly delay the pace of global warming and has to be accounted for in long-term climate predictions. The recent interest in dynamic, microphysical, chemical and climate impacts of volcanic eruptions is also excited by the fact these impacts provide a natural analog for climate geoengineering schemes involving the deliberate development of an artificial aerosol layer in the lower stratosphere to counteract global warming. In this talk, I will discuss these recently discovered volcanic effects and specifically pay attention to how we can learn about the hidden Earth-system mechanisms activated by explosive volcanic eruptions.

Apr 18 - Apr 25, 2021

Distributed second order methods with fast rates and compressed communication

Peter Richtarik, Professor, Computer Science

Apr 22, 12:00 - 13:00

KAUST

We develop several new communication-efficient second-order methods for distributed optimization. Our first method, NEWTON-STAR, is a variant of Newton's method from which it inherits its fast local quadratic rate. However, unlike Newton's method, NEWTON-STAR enjoys the same per iteration communication cost as gradient descent. While this method is impractical as it relies on the use of certain unknown parameters characterizing the Hessian of the objective function at the optimum, it serves as the starting point which enables us to design practical variants thereof with strong theoretical guarantees. In particular, we design a stochastic sparsification strategy for learning the unknown parameters in an iterative fashion in a communication efficient manner. Applying this strategy to NEWTON-STAR leads to our next method, NEWTON-LEARN, for which we prove local linear and superlinear rates independent of the condition number. When applicable, this method can have dramatically superior convergence behavior when compared to state-of-the-art methods. Finally, we develop a globalization strategy using cubic regularization which leads to our next method, CUBIC-NEWTON-LEARN, for which we prove global sublinear and linear convergence rates, and a fast superlinear rate. Our results are supported with experimental results on real datasets, and show several orders of magnitude improvement on baseline and state-of-the-art methods in terms of communication complexity.

Apr 11 - Apr 18, 2021

Apr 4 - Apr 11, 2021

Mar 28 - Apr 4, 2021

Mar 21 - Mar 28, 2021

The Convex Gaussian Min-Max Theorem: A Powerful Tool for the Analysis of Regularized Convex Optimization Problems

Tareq Al-Naffouri, Professor, Electrical and Computer Engineering

Mar 25, 12:00 - 13:00

KAUST

In modern large-scale inference problems, the dimension of the signal to be estimated is comparable or even larger than the number of available observations. Yet the signal of interest lies in some low-dimensional structure, due to sparsity, low-rankness, finite alphabet, ... etc. Non-smooth regularized convex optimization are powerful tools for the recovery of such structured signals from noisy linear measurements. Research has shifted recently to the performance analysis of these optimization tools and optimal turning of their hyper-parameters in high dimensional settings. One powerful performance analysis framework is the Convex Gaussian Min-max Theorem (CGMT). The CGMT is based on Gaussian process methods and is a strong and tight version of the classical Gordon comparison inequality. In this talk, we review the CGMT and illustrate its application to the error analysis of some convex regularized optimization problems.