Events

Sep 23 - Sep 17, 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 16 - Sep 10, 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.
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  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 9 - Sep 3, 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.
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  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.
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  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.

Sep 2 - Aug 27, 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.

Aug 5 - Jul 30, 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 8 - Jul 2, 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 10 - Jun 4, 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.
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  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 27 - May 21, 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 13 - May 7, 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).
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  A brief introduction to the obstacle problem regularity theory

  Prof. Eduardo Teixeira, Department of Mathematics, University of Central Florida (USA)

  May 8, 08:30 - May 11, 10:00

  B1 L4 R4102

  free boundary problems

  Abstract 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. Brief Biography Eduardo Teixeira is a Full
• 

  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.

May 6 - Apr 30, 2023

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  Modeling and Simulation of Semiconductor Equations: Insights and Applications

  Prof. Nella Rotundo, Department of Mathematics University of Florence, Italy

  May 3, 11:00 - 12:00

  B1 L4 R4102

  Abstract In this talk, we will discuss the modeling and simulation of semiconductor equations using three different formalisms. We will also describe the finite volume method and the Hybridizable Discontinuous Galerkin method as applied to semiconductor equations. These methods have proven to be highly effective in modeling semiconductor behavior, and have been used in a variety of applications such as integrated circuits, solar cells, and transistors. We will provide insights into the fundamental concepts behind these models and demonstrate their effectiveness through numerical simulations
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  Managing subsurface uncertainties in reservoir simulations: Applications to compositional flow and geothermal extraction

  Prof. Hussein Hoteit, Energy Resources and Petroleum Engineering, KAUST

  May 2, 16:00 - 17:00

  B2 L5 R5220

  Managing Subsurface reservoir Simulations compositional flow geothermal extraction

  Reservoir simulation is an essential tool in reservoir engineering, enabling effective management and optimization of reservoirs. Despite minimal changes in the fundamental sciences since the development of reservoir simulators, advancements in computational power and linear solvers have significantly improved the technology. However, the reliability and ability to improve decision quality of reservoir models remain debatable.
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  Sub-dimensional Mardia measures of multivariate skewness and kurtosis

  Joydeep Chowdhury, Postdoctoral Research Fellow, Statistics

  May 1, 12:00 - 13:00

  B9 L3 R3128

  The Mardia measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not reflect the sub-dimensional features of the distribution. Consequently, testing procedures based on these measures may fail to detect skewness or kurtosis present in a sub-dimension of the multivariate distribution. We introduce sub-dimensional Mardia measures of multivariate skewness and kurtosis, and investigate the information they convey about all sub-dimensional distributions of some symmetric and skewed families of multivariate distributions.

Apr 22 - Apr 16, 2023

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  CT-based COVID-19 Diagnosis and Prognosis through Novel AI Model Development

  Xin Gao, Professor, Computer Science

  Apr 18, 16:00 - 17:00

  B2 L5 R5220

  CT-based COVID-19 Diagnosis Prognosis novel methodology model development

  During the recent pandemic, urgent advances have been made by the scientific community in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis. In this talk, I will introduce our work on a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources.
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  Pore-scale modeling of flow and transport in deformable porous media

  S. Majid Hassanizadeh, Professor, Stuttgart University, Germany, Utrecht University, The Netherlands,

  Apr 17, 12:00 - 13:00

  B9 L3 R3128

  Pore-scale models are valuable tools for the investigation of various flow and transport processes in porous media, and upscaling from pore to core scales. Popular approaches include pore-network models (PNM), Lattice-Boltzman models (LBM), Smooth Particle Hydrodynamics (SPH), volume-of-fluid method (VOF), and grain-scale models (GSM).

Apr 15 - Apr 9, 2023

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  Reversal in the Stationary Prandtl Equations

  Prof. Nader Masmoudi

  Apr 13, 13:30 - 14:30

  B2 L5 R5220

  Reversal Stationary Prandtl Equations

  We investigate reversal and recirculation for the stationary Prandtl equations. Reversal describes the solution after the Goldstein singularity, and is characterized by regions in which $u > 0$ and $u 0$. The classical point of view of regarding the Prandtl equations as an evolution equation in $x$ completely breaks down since $u$ changes sign. Instead, we view the problem as a quasilinear, mixed-type, free-boundary problem. Joint work with Sameer Iyer.
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  High-order non-conforming finite elements for the Stokes equation

  Stefan Sauter, Professor, Institute of Mathematics, University of Zurich

  Apr 11, 16:00 - 17:00

  B2 L5 R5220

  High-order non-confirming Finite elements stokes equation

  We consider non-conforming discretizations of the stationary Stokes equation in two and three dimensions by Crouzeix-Raviart type elements. The original definition in the seminal paper by M. Crouzeix and P.-A. Raviart in 1973 is implicit and also contains substantial freedom for a concrete choice.
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  Robust and efficient algorithms for phase equilibrium calculations of reservoir fluids at constant moles, volume and temperature

  Shuyu Sun, Professor of Earth Science and Engineering، KAUST

  Apr 10, 12:00 - 13:00

  B9 L3 R3128

  Reservoir simulation usually involves the fluid flow of partially miscible multi-component multi-phase mixture in porous media. Phase behavior of fluid mixture is a crucial component of many multi-phase flow framework. Accurate modeling and robust computation of the phase behavior is essential for optimal design and cost-effective operations in petroleum reservoirs as well as in a petroleum processing plant. A typical problem formulation in phase behavior is two-phase constant volume flash, i.e., the two-phase phase-split under the constant temperature, moles, and volume.

Apr 8 - Apr 2, 2023

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  Tides: Introduction and Modelling - Part 3

  Prof. Clodoaldo Grotta Ragazzo, Institute of Mathematics and Statistics of the University of Sao Paulo

  Apr 5, 10:00 - 12:00

  B1 L4 R4102

  "Force-deformation laws, Love numbers, and the Association Principle". This lecture contains a discussion about realistic force-deformation relations used for celestial bodies, their empirical description by means of Love numbers, and their mathematical modelling by means of the "Association Principle''.
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  Numerical Methods for Energy Minimisation: Application to Machine Learning Algorithms

  Prof. Charalambos Makridakis, Director of the Institute of Applied and Computational Mathematics of FORTH, Greece

  Apr 4, 16:00 - 17:00

  B2 L5 R5220

  numerical methods energy minimization Applied Machine Learning

  In this talk, we discuss problems and numerical methods arising in the calculus of variations and energy minimization. Among numerous applications, energy minimization is a core element of Machine Learning algorithms. Within the field of nonlinear PDEs, the calculus of variations has received a lot of attention from the analysis point of view. Although quite interesting and challenging, the numerical analysis of these problems is much less developed.
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  Tides: Introduction and Modelling - Part 2

  Prof. Clodoaldo Grotta Ragazzo, Institute of Mathematics and Statistics of the University of Sao Paulo

  Apr 4, 10:00 - 12:00

  B1 L4 R4102

  "Translation, rotation and Deformation". In this lecturer, we will present the simplest possible model for the motion of two extended rigid bodies interacting by gravity.
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  Using the smithy of computational science to enhance the robustness and efficiency of complex fluid flow simulations.

  Matteo Parsani, Associate Professor, Applied Mathematics and Computational Science

  Apr 3, 12:00 - 13:00

  B9 L3 R3128

  This talk will review the recent shift in the construction and modern analysis of a large class of spatially and temporally adaptive methods whose properties are very close to our current analytical knowledge about hyperbolic or mixed hyperbolic-parabolic partial differential equations (PDEs) in computational physics. Thus these algorithms can be regarded as the elite methods in the field. Next, we will show examples of how the robustness and efficiency of the fully-discrete representation of PDEs can be enhanced using computational science's smithy, i.e., "modern" numerical analysis. The talk will showcase complex flow problems in aeronautics, aerospace, and automotive sectors, provide preliminary results in other fields, and present an outlook for future research directions where data science can currently be the linesman.
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  Tides: Introduction and Modelling - Part 1

  Prof. Clodoaldo Grotta Ragazzo, Institute of Mathematics and Statistics of the University of Sao Paulo

  Apr 3, 10:00 - 12:00

  B1 L4 R4102

  "Qualitative theory of tides and their effects." It contains a qualitative explanation of tides and their effects: phase lags, forces that dissipate energy but do not dissipat angular momentum, circularization of orbits, spin-orbit synchronization and collision.
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  Non-Abelian Braiding with Sound and Light

  Guancong Ma, Associate Professor, Department of Physics, Hong Kong Baptist University

  Apr 3, 09:30 - 10:30

  B9 L2 R2131

  Abstract Non-Abelian-based theories are one of the foundations of modern physics. For example, non-Abelian braiding realized by the dynamic winding of anyons is widely considered an important candidate for quantum computers. Operations defined by non-Abelian groups are non-commutative in character, meaning that the outcomes of sequential operations depend on the order of execution. Non-Abelian processes can be mathematically captured by unitary matrices, which can manifest as a Berry-phase matrix that connects holonomic adiabatic evolutions of multiple states in parameter space. Because the

Apr 1 - Mar 26, 2023

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  On the rate of convergence of the Inviscid Limit for 2D Incompressible Fluid

  Associate Professor Stefano Spirito, Department of Mathematics, University of LAquila, Italy

  Mar 30, 12:00 - 13:00

  B1 L4 R4102

  This talk reviews recent results concerning the inviscid limit for the 2D Euler equations with unbounded vorticity. In particular, by using techniques from the theory of transport equation with no smooth vector fields, we show that the solutions obtained in the vanishing viscosity limit satisfy a representation formula in terms of the flow of the velocity and that the strong convergence of the vorticity holds and we give a rate of convergence.
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  Scalable parallel solvers for cardiac reaction-diffusion models and applications

  Luca F. Pavarino, Professor, Department of Mathematics, Università degli Studi di Pavia

  Mar 28, 16:00 - 17:00

  B2 L5 R5220

  Applied Partial Differential Equations parallel simulations

  After a brief introduction to the field of Computational Cardiology and cardiac reentry, we introduce and study some scalable domain decomposition preconditioners for cardiac reaction-diffusion models, discretized with splitting semi-implicit techniques in time and isoparametric finite elements in space.
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  From Contact to Fluid Structure Interavtion Information Transfer between Non-matching meshes for Coupled Simulations

  Prof. Rolf Krause, Università della Svizzera italiana

  Mar 27, 12:00 - 13:00

  B9 L3 R3128

  The transfer of information between non-matching meshes is an important ingredient for coupled multi-physics simulations. For most coupled problems information, such as displacements or stresses, has to be exchanged between, two different meshes. As an example consider fluid-structure interaction (FSI) problem, where information has to be transferred between a fluid and an elastic body (the "S"tructure in FSI).
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  Mathematics of Emergent Phenomena in Networks and Collective Motion

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

  Mar 26, 12:00 - 13:00

  B9 L3 R3131

  Emergence of nontrivial patterns via collective actions of many individual entities is an ever-present phenomenon in physics, biology and social sciences. It has numerous applications in engineering, for instance, in swarm robotics. I shall demonstrate how tools from mathematical modeling and analysis help us gain understanding of fundamental principles and mechanisms of emergence. I will present my recent results in consensus formation and flocking models, taking into account their realistic aspects - noise, latency, finite speed of information propagation and anticipation. Moreover, I will introduce a continuum modeling framework for biological network formation, where emergence takes place through the interaction of structure and medium. The models are formulated in terms of ordinary, stochastic and partial differential equations. I shall explain how mathematical analysis of the respective models contributes to the understanding of how individual rules generate and influence the patterns observed on the global scale. Finally, I will explain how requirements on robustness of the network can be incorporated into the mathematical model.

Mar 25 - Mar 19, 2023

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  Proximal Algorithms for Large-Scale Convex Non-smooth Optimization

  Laurent Condat, Senior Research Scientist, Computer Science

  Mar 21, 16:00 - 17:00

  B2 L5 R5220

  algorithms stochastic algorithm

  Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.
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  On Fourier methods and machine learning techniques in Computational Finance.

  Prof. Kees Oosterlee, Utrecht University

  Mar 20, 12:00 - 13:00

  B9 L3 R3128

  In this presentation we will explain how we can solve linear, semi-linear as well as nonlinear partial differential equations by the concept of backward stochastic differential equations and Fourier cosine expansions. We will discuss the highly efficient pricing of financial options in the Fourier context by means of the COS method. Particularly, we also present a new jump-diffusion process, the Heston-Queue-Hawkes (HQH) model, combining the well-known Heston model and the recently introduced Queue-Hawkes (Q-Hawkes) jump process. Like the Hawkes process, the HQH model can capture the effects of self-excitation and contagion of stock prices.
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  5th KAUST CEMSE MaS Workshop on Modelling and Simulation

  Speakers from KAUST, Melbourne, Utrecht, Karlsruhe, Erlangen, Brisbane, Langensteinbach, Lugano, Frankfurt, Italy

  Mar 20, 09:00 - Mar 25, 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 Simulating unsteady CO 2 flow through brine-saturated cross-bedded sandstones: towards relative permeability curves for unstable displacement On Fourier methods and machine learning techniques in Computational Finance Pore-scale modelling of flow and transport in porous media Uncertainty Quantification of Density-Driven Groundwater Flow Simulation of unsaturated Density-Driven flow with a phreatic surface Modeling and Large-scale Simulation of Carbon Dioxide

Mar 18 - Mar 12, 2023

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  Gravitational Collapse of Self-similar stars

  Dr. Matthew Schrecker, Departments of Mathematics, University College London

  Mar 16, 16:00 - 17:00

  B1 L4 R4102

  Nonlinear Partial Differential Equation Gravitational collapse UCL

  The Euler-Poisson equations give the classical model of a self-gravitating star under Newtonian gravity. It is widely expected that, in certain regimes, initially smooth initial data may give rise to blow-up solutions, corresponding to the collapse of a star under its own gravity. In this talk, I will present recent work with Yan Guo, Mahir Hadzic and Juhi Jang that demonstrates the existence of smooth, radially symmetric, self-similar blow-up solutions for this problem. I will also comment on the stability of the obtained solution. At the heart of the analysis is the presence of a sonic point, a singularity in the self-similar model that poses serious analytical challenges in the search for a smooth solution.