Events

Nov 26 - Dec 2, 2023

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  Basic Algebraic Structures

  Prof. Efim Zelmanov

  Nov 29, 14:00 - 15:00

  B3 L5 R5220

  Structures Galois

  We will give an overview of the development of Abstract Algebra from Galois to our time. The talk will be accessible to general audience with basic mathematical background.
• 

  Numerical approximation of PDEs on complex geometries: fluid-structure interaction problems and virtual element methods

  Fabio Credali, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Nov 28, 16:30 - 18:30

  KAUST

  The studies in numerical approximation of partial differential equations are characterized by the necessity of managing complex geometries and their discretization. We focus our attention on two different fields where complex geometries are very common: the mathematical modeling of fluid-structure interaction problems and the family of virtual element methods.

Nov 19 - Nov 25, 2023

• 

  Efficient Inference for Joint Models of Multivariate Longitudinal and Survival Data Using INLAjoint

  Denis Rustand, Postdoctoral Research Fellow (former), Statistics

  Nov 23, 12:00 - 13:00

  B9 L2 H2

  Clinical research often requires the simultaneous study of longitudinal repeated measurements and time-to-event (i.e., survival) data. Joint models, which can combine these two types of data, are invaluable tools in this context.

Nov 12 - Nov 18, 2023

• 

  Multiscale Learning of High-dimensional Cell Fate

  Prof. Qing Nie, Department of Mathematics, Department of Developmental and Cell Biology, NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine

  Nov 16, 15:30 - 17:00

  B9 L4 R4225

  Abstract Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. The recent single-cell genomics technology provides an unprecedented opportunity to profile cells for all their genes. While those measurements provide high-dimensional gene expression profiles for all cells, it requires fixing individual cells that lose many important spatiotemporal information. Is it possible to infer temporal relationships among cells from single or multiple snapshots? How to recover spatial
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  How do periodic structures affect nonlinear waves?

  David Ketcheson, Professor, Applied Mathematics and Computational Science

  Nov 16, 12:00 - 13:00

  B9 L2 H2

  We are all familiar with the tendency of water waves to break in shallow water, for instance at the beach. Indeed, breaking is a universal behavior of solutions to first-order nonlinear hyperbolic PDEs, and manifests itself in phenomena ranging from traffic jams to shock waves.
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  Tensor-train Methods for Partial Differential Equations and its application to a Neutron Transport Problem

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

  Nov 14, 15:30 - 17:00

  B9 L4 R4225

  Partial Differential Equations

  Tensor network techniques are known for their ability to approximate low-rank structures and beat the curse of dimensionality. They are also increasingly acknowledged as fundamental mathematical tools for efficiently solving high-dimensional Partial Differential Equations (PDEs). In this talk, we present a novel method that incorporates the Tensor Train (TT) and Quantized Tensor Train (QTT) formats for the computational resolution of time-independent Boltzmann Neutron Transport equations (BNTEs) in Cartesian coordinates.
• 

  The Partition of Unity Finite Element Method for the Schrödinger Equation

  Francesca Gardini, Associate Professor, Dipartimento di Matematica, Università di Pavia, Italy

  Nov 14, 14:30 - 15:30

  B9 L4 R4225

  A Schrödinger equation for the system’s wavefunctions in a parallelepiped unit cell subject to Bloch-periodic boundary conditions must be solved repeatedly in quantum mechanical computations to derive the materials’ properties.

Nov 5 - Nov 11, 2023

• 

  A Brief Overview of Arabic NLP

  Prof. Sakhar B. Alkhereyf

  Nov 7, 15:30 - 17:00

  B5 L5 R5209

  nlp Arabic natural language processing

  Arabic, being one of the six official languages of the United Nations and the sixth most commonly spoken language across the globe, holds a significant stance in the linguistic landscape. The diglossic nature of Arabic, where Classical Arabic (CA), Modern Standard Arabic (MSA), and various dialects coexist, coupled with its complex morphological structure, presents a unique set of challenges and opportunities for the domain of Natural Language Processing (NLP).
• 

  Adaptive Optimization Algorithms for Machine Learning

  Slavomir Hanzely, Ph.D. Student, Applied Mathematics and Computational Science

  Nov 7, 10:00 - 11:00

  B1 L4 R4214

  artificial intelligence machine learning

  Machine learning assumes a pivotal role in our data-driven world. The increasing scale of models and datasets necessitates quick and reliable algorithms for model training. This dissertation investigates adaptivity in machine learning optimizers.

Oct 29 - Nov 4, 2023

• 

  Free Boundary Problems in Science and Engineering

  Rafayel Teymurazyan, Research Scientist, Applied Mathematics and Computational Science

  Nov 2, 12:00 - 13:00

  B9 L2 H2

  Free boundary problems arise naturally in a range of mathematical models that describe physical, biological or financial phenomena, such as the melting of ice into water, the dynamics of a population or the behavior of stock markets, to mention just a few.
• 

  Stochastic Optimal Control with Exercise Rate Optimization and Markovian Projections: Pricing American Options and Importance Sampling Applications

  Raul Tempone, Professor, Applied Mathematics and Computational Science

  Oct 31, 15:30 - 17:00

  B5 L5 R5209

  sampling Markovian projection

  This talk begins with the problem of pricing American basket options in a multivariate setting. In high dimensions, nonlinear PDE methods for solving the corresponding Hamilton-Jacobi-Bellman (HJB) equation become expensive due to the curse of dimensionality.
• 

  Quantum Computing with Xanadu

  Catalina Albornoz, Quantum Community Manager at Xanadu

  Oct 31, 15:30 - 17:00

  KAUST

  quantum computing PennyLane photonic architecture

  Xanadu is a Canadian quantum computing company with the mission to build quantum computers that are useful and available to people everywhere. Xanadu is one of the world’s leading quantum hardware and software companies and also leads the development of PennyLane, an open-source software library for quantum computing and application development.

Oct 22 - Oct 28, 2023

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  Spatio-temporal Bayesian analysis of excess mortality in 5 European countries in 2020

  Prof. Virgilio Gómez-Rubio, Department of Mathematics, Universidad de Castilla-La Mancha, Spain

  Oct 26, 12:00 - 13:00

  B9 L2 H2

  Abstract The COVID-19 pandemic produced excess mortality in many countries in 2020. In my talk, I will illustrate our use of Bayesian models and R software for the analysis of total deaths and population data, combined with environmental data, to the study of excess mortality in 2020. The study analyzes total deaths by age groups and sex in 5 European countries, and provides estimates of excess mortality at different levels of aggregation (province, autonomous community and country). For both data analysis and visualization of the results we have used the statistical software R. In addition
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  A Variational Theory of Aero-hydrodynamics

  Haithem Taha, Associate Professor

  Oct 24, 16:00 - 17:00

  B5 L5 R5209

  Euler equations aerodynamics Variational Navier-Stokes

  The Euler equation does not possess a unique solution for the flow over a two-dimensional object. This problem has serious repercussions in aerodynamics; it implies that the inviscid aero-hydrodynamic lift force over a two-dimensional object cannot be determined from first principles; a closure condition must be provided. The Kutta condition has been ubiquitously considered for such closure in the literature, even in cases where it is not applicable (e.g. unsteady).

Oct 15 - Oct 21, 2023

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  Latent Gaussian Spatial Modelling with Change of Support

  Erick Chacon Montalvan, Postdoctoral fellow, Statistics Geohealth Group, KAUST

  Oct 19, 12:00 - 13:00

  B9 L2 H2

  Spatial data analysis commonly needs to deal with spatial data derived from multiple sources (e.g. satellites, stations, survey samples) with different supports, but associated with the same properties of a spatial phenomenon under interest. Usually, predictors are also measured on different spatial supports than the response variable.

Oct 8 - Oct 14, 2023

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  Quantinuum’s Full Stack Approach to Quantum Computing

  Dr. Jenni Strabley, Anand Shah, Dr. Brian Neyenhuis

  Oct 12, 16:00 - 18:00

  KAUST

  Quantinuum

  Quantinuum is the world’s largest integrated quantum computing company. Quantinuum has developed the H-Series quantum computers based on a trapped-ion architecture using the unique and highly-scalable QCCD architecture (QCCD = Quantum Charge-Coupled Device), and released its first two generations: the System Model H1 and H2. The H-Series QPUs are well-known for their superior, low noise performance and differentiating capabilities such as all-to-all connectivity and mid-circuit measurement with very low qubit cross talk. Dr. Brian Neyenhuis will discuss how the QCCD architecture enables these differentiators and what researchers can do with the H-Series quantum computers that they cannot do on other commercially available quantum computers. As a full stack quantum computing company, Quantinuum also makes TKET, an open source SDK compiler and optimizer, as well as algorithm and application layers for applications in Machine Learning, Finance, Chemistry, and modeling of Quantum Systems. Anand Shah will review the application and algorithm activities at Quantinuum.
• 

  Space–Time Multiscale Modeling and Numerics of Sorption Kinetics

  Clarissa Astuto, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

  Oct 12, 12:00 - 13:00

  B9 L2 H2

  In this talk we propose and validate a Space Multiscale model for the description of particle diffusion in the presence of trapping boundaries. We start from a drift diffusion equation in which the drift term describes the effect of bubble traps, and it is simulated by the Lennard–Jones potential.
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  Models and methods for multifluid flow in Lagrangian coordinates

  Prof. Giovanni Russo, Full Professor of Numerical Analysis Department of Mathematics and Computer Science, University of Catania, Italy

  Oct 10, 14:30 - 15:30

  B1 L4 R4102

  Abstract Multifluid has attracted a lot of attention in recent years. In particular, stratified fluids composed by a sequence of alternate layers show interesting macroscopic properties, which may be quite different from those of the individual fluids which constitute the stratified system. On a macroscopic scale, such a system can be considered a sort of fluid metamaterial. Several models have been proposed to describe the stratified system as a single effective fluid. After an introduction, we present two homogenized models of the stratified fluid system, which are based on isentropic

Oct 1 - Oct 7, 2023

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  How many sample points do we need for least squares?

  Diogo Gomes, Professor, Applied Mathematics and Computational Science

  Oct 5, 12:00 - 13:00

  B9 L2 H2

  Abstract The goal of the least squares method is to find the best linear combination of certain given functions to fit given data. A typical example is the following: given pairs $(x_i, y_i)$, find the coefficients, $a$ and $b$ that minimize the error $\sum_i (y_i-a -b x_i)^2$. In this case, if at least two of the coordinates $x_i$ are distinct, we can always find coefficients $a$ and $b$. However, if the $x_i$ are randomly sampled, these coordinates may be extremely close, creating large errors in the estimated values of $a$ and $b$. In this talk, we investigate lower bounds on the minimum
• 

  Several derivations of the degenerate Cahn-Hilliard equation via singular limits

  Dr. Jakub Skrzeczkowski,Mathematical Institute, University of Oxford

  Oct 4, 16:00 - 17:00

  B1 L4 R4214

  The degenerate Cahn-Hilliard equation, initially introduced in material science, is nowadays used in several different fields, including biology (tumor growth, cell-cell adhesion) and fluid dynamics (high-friction limit in the Euler-Korteweg equation).
• 

  Multiphase flow in heterogeneous geologic media

  Prof. Stephan K. Matthai, Chair of Reservoir Engineering, The University of Melbourne at Parkville

  Oct 3, 16:00 - 17:00

  B5 L5 R5209

  Multiphase flow heterogeneous geologic media

  Heterogeneous permeability, porosity, capillary pressure, and strength are key characteristics of sedimentary and volcanic rocks in geologic reservoirs. In many cases, the heterogeneity is due to nested geological features like the mm-scale coarse-fine intercalations of cross-bedded sandstones, or the km-scale channel, levy, and overbank facies associations in a fluvial environment of deposition. Where such rocks have experienced an overprint by deformation or reactive fluid flow, initial heterogeneities might be amplified even further: fracturing can impart long-range spatial correlations on the permeability structure, strongly amplifying permeability anisotropy, even in the absence of a noticeable increase of porosity.

Sep 24 - Sep 30, 2023

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  Decision Trees for Fault Diagnosis in Circuits and Switching Networks - 2023-09-28

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  Sep 28, 12:00 - 13:00

  B9 L2 H2

  We study theoretical problems of fault diagnosis in circuits and switching networks, which are among the most fundamental models for computing Boolean functions.
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  Device accelerated solvers for PDEs. Current status and future perspectives

  Stefano Zampini, Senior Research Scientist, Hierarchical Computations on Manycore Architectures

  Sep 26, 16:00 - 17:00

  B5 L5 R5220

  Shaheen GPU supercomputing petsc PDEs

  In the next months, KAUST expects to place into service Shaheen-3, a supercomputer with a GPU partition whose planned 1 Exaflop/s HPL AI capability would rank it in the Top 6 globally if delivered today.

Sep 17 - Sep 23, 2023

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  Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data

  Iris Ivy Gauran, Postdoctoral Research Fellow, Biostatistics Group

  Sep 21, 12:00 - 13:00

  B9 L2 H2

  Cross-validation is an algorithmic technique extensively used for estimating the prediction error, tuning the regularization parameter, and choosing between competing predictive rules.
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  Breiman’s Samplers or Models? There is a little but important difference: Models, including priors, can be wrong!

  Dr. Yannis Yatracos

  Sep 20, 15:00 - 16:00

  B1 L2 R2202

  Breiman (2001) urged statisticians to provide tools when the data, X=s(θ,Y); sampler s is available as Black-Box, parameter θεΘ, Y is random, either observed or latent. The paper’s discussants, D. R. Cox and B. Efron, looked at the problem as X-prediction, surprisingly neglecting the statistical inference for θ, and disagreed with the main thrust of the paper.
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  Additive Schwarz Methods for Convex Optimization - Theory and Applications

  Jongho Park, Research Scientist, Applied Mathematics and Computational Science

  Sep 19, 16:00 - 17:00

  B5 L5 R5209

  Additive Schwarz methods convex optimization

  This talk is devoted to additive Schwarz methods for convex optimization. First, we propose an abstract framework for additive Schwarz methods for convex optimization. The framework's flexibility allows it to handle composite optimization problems and inexact local solvers. Moreover, it establishes a sharp convergence theory that agrees with the classical theory when addressing linear problems.
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  Universal light encoders: artificial intelligent optical hardware for real-time hyperspectral imaging and ultrasensitive detection

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Sep 17, 12:00 - 13:00

  B9 L2 H2

  Metasurfaces machine learning

  In this talk, I will review the fundamentals and application of this technology in various areas, ranging from the inverse design of ultra-flat optical components to HyplexTM, an innovative camera for acquiring and processing high-resolution hyperspectral videos in real-time at 30 frames per second.

Sep 10 - Sep 16, 2023

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  Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function

  Maicon J. Karling, Postdoctoral Research Fellow, Statistics

  Sep 14, 12:00 - 13:00

  B9 L2 H2

  Goodness-of-fit tests determine how well a set of observed data fits a particular probability distribution. They can also show if some categorical variable follows a hypothesized family of distributions.
• 

  Stability and Asymptotics in Electrohydrodynamics

  Nuno J. Alves, Ph.D. Student, Applied Mathematics and Computational Science

  Sep 12, 15:00 - 17:00

  B1 L4 R4214

  Relative Energy Analysis relative energy method

  This dissertation focuses on the relative energy analysis of two-species fluids composed of charged particles. In particular, it explores several applications of the relative energy method to Euler-Poisson systems, enabling a comprehensive stability analysis of these systems.

Sep 3 - Sep 9, 2023

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  From Euler flows with friction to gradient flows and applications

  Athanasios Tzavaras, Professor, Applied Mathematics and Computational Science

  Sep 7, 12:00 - 13:00

  B9 L2 H2

  I will review some works on the high-friction limit (or small mass approximation) from Euler flows to advection-diffusion systems that are gradient flows, and related asymptotic problems in fluid mechanics. The formulation exploits the variational structure of compressible Euler flows and is connected to the interpretation of nonlinear Fokker-Planck systems as gradient flows in Wasserstein distance.
• 

  Mean Field Games price formation models

  Julian David Gutierrez Pineda, Ph.D. Student, Applied Mathematics and Computational Science

  Sep 6, 14:00 - 16:00

  B1 L4 R4214

  mean-field games Applied Machine Learning

  This thesis studies mean-field games (MFGs) models of price formation. The thesis focuses explicitly on a MFGs price formation model proposed by Gomes and Saúde. The thesis is divided into two parts. The first part examines the deterministic supply case, while the second part extends the model to incorporate a stochastic supply function. We explore different approaches, such as Aubry-Mather theory, to study the properties of the MFGs price formation model and alternative formulations using a convex variational problem with constraints. We propose machine-learning-based numerical methods to approximate the solution of the MFGs price formation model in the deterministic and stochastic setting.
• 

  Universal Portfolio Algorithm and Confidence Sequences

  Francesco Orabona, Associate Professor, Computer Science

  Sep 5, 16:00 - 17:00

  B5 L5 R5209

  Universal Portfolio algorithm

  In this talk, I will describe a surprising application for a known online learning problem and its optimal algorithm.

Aug 27 - Sep 2, 2023

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  Efficient Intensity Estimation Using Adaptive Bandwidths on Linear Networks: A Study and Application in Traffic Accident Analysis

  Jonatan A. González, Postdoctoral Research Fellow, Statistics

  Aug 31, 12:00 - 13:00

  B9 L2 H2

  Estimating first-order intensity functions is crucial in the analysis of point patterns on linear networks, but selecting suitable bandwidths for non-parametric methods remains challenging. We propose an adaptive intensity estimator for the heating kernel that adjusts bandwidths based on data points, a novel approach in this context.
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  Finite element approximations for variable-order fractional diffusion problems

  Prof. Wenyu Lei, University of Electronic Science and Technology of China

  Aug 29, 16:00 - 17:00

  B4 L5 R5209

  Finite elements Variable-order fractional diffusion problems

  We propose a finite element scheme for fractional diffusion problems with varying diffusivity and fractional order. A number of challenges are encountered when discretizing such problems. The first is the heterogeneous kernel singularity in the fractional integral operator. The second comes from the dense discrete operator with its quadratic growth in memory footprint and arithmetic operations. To address these challenges, we propose a strategy that decomposes the system matrix into three components. The first is a sparse matrix that handles the singular near-field separately and is computed by adapting singular quadrature techniques available for the homogeneous case to the case of spatially variable order.

Jul 30 - Aug 5, 2023

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  Simulation of the drawdown and its duration in Lévy models via stick-breaking Gaussian approximation

  Prof. Aleksandar Mijatovic, Statistics, University of Warwick

  Aug 2, 15:00 - 16:00

  B1 L2 R2202

  Abstract In this talk, I will describe a computational method for expected functionals of the drawdown and its duration in exponential Lévy models. It is based on a novel simulation algorithm for the joint law of the state, supremum and time the supremum is attained of the Gaussian approximation for a general Lévy process. We bound the bias for various locally Lipschitz and discontinuous payoffs arising in applications and analyse the computational complexities of the corresponding Monte Carlo and multilevel Monte Carlo estimators. Monte Carlo methods for Lévy processes (using Gaussian

Jul 2 - Jul 8, 2023

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  Axisymmetric flows with swirl for Euler and Navier-Stokes equations

  Ioanna Mousikou, Ph.D. Student, Applied Mathematics and Computational Science

  Jul 6, 15:00 - 16:00

  B1 L4 R4214

  We consider the incompressible axisymmetric Navier-Stokes equations as an idealized model of tornado-like flows. Assuming that an infinite vortex line that interacts with a boundary surface resembles the tornado core, we look for stationary self-similar solutions of the axisymmetric Euler and the axisymmetric Navier-Stokes equations emphasizing the connection among them as the viscosity ν → 0.

Jun 4 - Jun 10, 2023

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  Preconditioning for Kernel Matrices

  Edmond Chow, Professor and Associate Chair, School of Computational Science and Engineering, Georgia Institute of Technology

  Jun 6, 16:00 - 17:00

  B2 L5 R5220

  Preconditioning Kernel Matrices

  Coffee Time: 15:30 - 16:00. Kernel matrices can be found in computational physics, chemistry, statistics, and machine learning. Fast algorithms for matrix-vector multiplication for kernel matrices have been developed, and is a subject of continuing interest, including here at KAUST. One also often needs fast algorithms to solve systems of equations involving large kernel matrices. Fast direct methods can sometimes be used, for example, when the physical problem is 2-dimensional. In this talk, we address preconditioning for the iterative solution of kernel matrix systems. The spectrum of a kernel matrix significantly depends on the parameters of the kernel function used to define the kernel matrix, e.g., a length scale.
• 

  Panel Discussion: A Roadmap for Bringing to Earth the Energy of the Stars

  Prof. Amitava Bhattacharjee, Dr. Michael Zarnstorff, Dr. Spencer Pitcher, Mr. Richard Carty

  Jun 5, 16:15 - 16:45

  B20 Auditorium

  roadmap Earth energy Stars

  “Practical fusion power is thirty years off and always will be.” Most people are instantly enraptured at the thought that a single glass of water will provide enough fusion fuel for their lifetime – if only it could be safely unlocked – and most of us have heard, as well, that we should not set our watches for when this will occur.
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  Lecture 2: The Coming Dawn of Commercial Fusion Energy

  Prof. Amitava Bhattacharjee, Astrophysical Sciences, Princeton University

  Jun 5, 15:45 - 16:15

  B20 Auditorium

  Generating fusion power from a stellarator is an exciting scientific and engineering challenge, one that promises to produce a stable, green, and nearly perennial energy source for mankind. To do so economically and safely will require a combination of several interdisciplinary breakthroughs including extreme scale computing (with adroit use of artificial intelligence and machine learning), high-temperature superconductors, materials science research, and robotics, to name a few. Overcoming this challenge will need a worldwide effort, involving academia, national laboratories, and industry, and contributions from diverse economies and people.
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  Lecture 1: Hidden Symmetries and Fusion Energy

  Prof. Amitava Bhattacharjee, Astrophysical Sciences, Princeton University

  Jun 5, 14:30 - 15:30

  B20 Auditorium

  Hidden Symmetries Fusion energy

  The most compelling transformational use of magnetically confined, high-temperature plasma is to realize sustained fusion energy. The tokamak, which is the leading magnetic confinement concept in the world today, first realized in 1958, has the geometry of a torus and toroidal symmetry, giving it good confinement properties. Nevertheless, the tokamak has a number of unresolved stability issues related to its current-carrying plasma that may be obstacles to its ultimate success. In contrast, in the stellarator, the confining magnetic field is mostly produced by external current-carrying coils.

May 21 - May 27, 2023

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  Multiscale numerical methods. Theory and Practice

  Yalchin Efendiev, Professor, Institute for Scientific Computation, Texas A and M University

  May 21, 12:00 - 13:00

  B9 L3 R3128

  numerical methods

  In this talk, I will talk about general approaches for multiscale modeling (closely related to porous media applications). I will mainly focus on numerical approaches, where multiscale finite element basis functions are constructed and used in approximating the solution. In these approaches, macroscopic equations are formed via some variational formulations of the problem. I will discuss how these approaches are used in deriving, so called upscaling techniques and the relation to well known upscaling methods. The concepts discussed in the talk are used for linear and nonlinear problems. I will discuss some applications.
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  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023

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

  May 21, 08:00 - Jun 1, 17:00

  B9

  stochastic algorithms statistical learning machine learning

  Dear Kaustians, We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2023, taking place at KAUST, Building 9, from May 21 to June 1, 2023. Following the highly successful 2022 edition, this year's workshop promises to be another engaging and insightful event for researchers, faculty members, and students interested in stochastic algorithms, statistical learning, optimization, and approximation. The 2023 workshop aims to build on the achievements of last year's event, which featured 28 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022, attendees had the opportunity to learn from through insightful talks, interactive mini-courses, and vibrant poster sessions. This year, the workshop will once again showcase contributions that offer mathematical foundations for algorithmic analysis or highlight relevant applications. Confirmed speakers include renowned experts from institutions such as Ecole Polytechnique, EPFL, Université Pierre et Marie Curie - Paris VI, and Imperial College London, among others.

May 7 - May 13, 2023

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  Monotonicities and master equations in mean field games - Part 3

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University (UK)

  May 11, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.
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  A brief introduction to the obstacle problem regularity theory - Part 3

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 11, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
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  On some nonlinear elliptic/parabolic systems arising in mechanics and turbulence

  Prof. Luigi Carlo Berselli

  May 9, 16:00 - 17:00

  B2 L5 R5220

  nonlinear linear elliptic parabolic Systems Mechanics Turbulence

  I consider the vector counterpart of the classical p-Laplace and p-heat equations which are some of the building blocks for the mathematical description of non-linear plasticity, non-Newtonian fluids, and turbulent eddy viscosity models. I will discuss results of “natural regularity” and their role in the study of the well-posedness of the nonlinear pdes as well as in the theory of convergence for space-time discretisation methods. In particular, I will present the “A-approximation" method which generalises results by Necas and which reduces the problem to a family of linear ones. Coffee Time: 15:30 - 16:00
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  Monotonicities and master equations in mean field games - Part 2

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University (UK)

  May 9, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule.
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  A brief introduction to the obstacle problem regularity theory - Part 2

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 9, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.
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  Process Monitoring Techniques and Applications

  Fouzi Harrou , Senior Research Scientist, Statistics

  May 8, 12:00 - 13:00

  B9 L3 R3128

  Abstract Effective process monitoring is crucial to ensure engineering and environmental plants' reliable and safe operation. If anomalies in engineering or environmental plants are not detected promptly, they can affect plants' productivity, profitability, and safety. Both model-based and data-based process monitoring techniques have proven themselves in practice over the past four decades. This talk will present a model-based approach for anomaly detection in a linear model with a bounded nuisance parameter and a data-based process monitoring example using machine learning. Three real-world
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  Monotonicities and master equations in mean field games - Part 1

  Alpar Meszaros, Assistant Professor, Department of Mathematical Sciences, Durham University, UK

  May 8, 10:30 - 12:00

  B1 L4 R4102

  The theory of mean field games (MFG for short) aims to study limiting behavior of Nash equilibria of (stochastic) differential games when the number of agents tends to infinity. While in general existence of MFG Nash equilibria can be established under fairly general assumptions, uniqueness is the exception rather than the rule. For finite horizon mean field games uniqueness typically holds if the time horizon is small (or the data satisfies other smallness conditions).
• 

  A brief introduction to the obstacle problem regularity theory - Part 1

  Eduardo Teixeira, Full Professor, Department of Mathematics, University of Central Florida (USA)

  May 8, 08:30 - 10:00

  B1 L4 R4102

  How does an elastic membrane lie on a given obstacle? This naive looking question hides a beautiful mathematical theory integrating powerful tools with far-reaching applications. In this mini-course we will discuss the obstacle problem as a free boundary model. All necessary tools will be carefully constructed from scratch. We will mainly focus on: optimal regularity of solutions, non-degeneracy estimates, weak geometric-measure properties of the free boundary, classification of global profiles, and differentiability of the free boundary.