Events

Mar 16 - Mar 22, 2025

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  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Sciences

  Mar 20, 12:00 - 13:00

  B9, L2, R2325

  Dynamic programming is an efficient technique to solve optimization problems. It is based on decomposing the initial problem into simpler ones and solving these sub-problems beginning from the simplest ones. A conventional dynamic programming algorithm returns an optimal object from a given set of objects.

Mar 2 - Mar 8, 2025

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  The Ball-Proximal (=“Broximal”) Point Method: a New Algorithm, Convergence Theory, and Applications

  Kaja Gruntkowska, PhD student, Statistics, KAUST

  Mar 6, 12:00 - 13:00

  B9, L2, R2325

  Non-smooth and non-convex global optimization poses significant challenges across various appli- cations, where standard gradient-based methods often struggle. We propose the Ball-Proximal Point Method, Broximal Point Method, or Ball Point Method (BPM) for short – a novel algorith- mic framework inspired by the classical Proximal Point Method (PPM) (Rockafellar, 1976), which, as we show, sheds new light on several founda- tional optimization paradigms and phenomena, including non-convex and non-smooth optimiza- tion, acceleration, smoothing, adaptive stepsize selection, and trust-region methods.
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  Enhance Neural Network Training for Complex PDEs: Algorithms, Architectures, and Integration with Traditional Methods

  Dr. Yahong Yang, Postdoctoral Scholar, Pennsylvania State University, USA

  Mar 2, 12:00 - Mar 3, 01:00

  Auditorium between Building 2 - 3, L0 R0215

  This talk explores recent research on improving neural network methods for solving partial differential equations (PDEs) by addressing training challenges through novel algorithms, architectures, and integration with traditional numerical techniques.

Feb 23 - Mar 1, 2025

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  Ringmaster ASGD: The First Asynchronous SGD with Optimal Time Complexity

  Artavazd Maranjyan, PhD Student, AMCS, KAUST

  Feb 27, 12:00 - 13:00

  B9, L2, R2325

  Asynchronous Stochastic Gradient Descent (Asynchronous SGD) is a cornerstone method for parallelizing learning in distributed machine learning. However, its performance suffers under arbitrarily heterogeneous computation times across workers, leading to suboptimal time complexity and inefficiency as the number of workers scales.
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  Theoretical Foundations of Efficient and Scalable Graph Learning

  Dr. Ziang Chen, Instructor, Applied Mathematics, MIT, USA

  Feb 24, 12:00 - 13:00

  Auditorium between Building 2 - 3, L0 R0215

  Graph Neural Networks

  This talk addresses key challenges in graph neural networks (GNNs), including expressive power and model depth, by theoretically analyzing and providing solutions in three areas: universal approximation of linear program properties, strong expressive power of subgraph GNNs on graphs with bounded cycles, and the role of residual connections in mitigating oversmoothing and enabling deeper models.
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  Adapting Foundation Models: From Reinforcement Learning to Multivariate Time Series Forecasting

  Abdelhakim Benechehab, Ph.D. Student, EURECOM, France

  Feb 24, 12:00 - 13:00

  B4 L5 R5220

  This talk explores adapting foundation models for reinforcement learning and multivariate time series forecasting, introducing DICL for handling multivariate data in RL and AdaPTS for probabilistic time series forecasting, both showing significant performance improvements over baseline methods.

Feb 16 - Feb 22, 2025

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  Derivative-Free Methods in Nonconvex Optimization with and Without Noise

  Boris Mordukhovich, Distinguished University Professor, Wayne State University

  Feb 20, 12:00 - 13:00

  B9, L2, R2325

  This talk addresses the study of nonconvex derivative-free optimization problems, where only information of either smooth objective functions or their noisy approximations is available. General derivative-free methods are proposed for minimizing differentiable (not necessarily convex) functions with globally Lipschitz continuous gradients, where the accuracy of approximate gradients is interacting with stepsizes and exact gradient values.
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  Convergence of Descent Methods under Polyak- Lojasiewicz-Kurdyka Properties

  Boris Mordukhovich, Distinguished Professor, Mathematics, Wayne State University, USA

  Feb 19, 16:00 - 17:00

  B1 L3 R3119

  optimization algorithm

  This talk presents a novel convergence analysis for a class of descent methods in nonsmooth and nonconvex optimization under the Polyak-Lojasiewicz-Kurdyka properties, proving finite termination under certain conditions and revealing incompatibilities between these properties and gradient Lipschitz continuity in certain frameworks.
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  Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

  Dr. Harri Hakula, Principal University Lecturer, Mathematics and Systems Analysis, Aalto University, Finland

  Feb 18, 16:00 - 17:00

  B1 L3 R3119

  PDEs numerical methods optimization

  This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.

Feb 9 - Feb 15, 2025

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  Recent Advances in Infinity Laplacian Equations

  Aelson Sobral, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Feb 13, 12:00 - 13:00

  B9, L2, R2325

  The infinity Laplacian is a highly degenerate, nonlinear, second-order operator that arises in the study of optimal Lipschitz extensions and tug-of-war games, where two players alternately choose directions to move. In this presentation, we will provide a comprehensive motivation for key aspects of the infinity Laplacian and report on recent advances in the qualitative properties of its evolutionary counterpart.
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  Mapping Properties of Neural Networks and Inverse Problems

  Prof. Matti Lassas, Applied Mathematics, University of Helsinki, Finland

  Feb 12, 18:00 - 19:00

  B1 L4 R4214

  Neural Networks generative models

  This talk will explore the injectivity and universal approximation properties of neural networks, focusing on one-to-one mappings and the approximation of probability measures using compositions of invertible flow networks and injective layers, with applications in inverse problems, imaging, and generative models.
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  Bridging Optimal Control and Modern AI Techniques

  Tingwei Meng, Assistant Professor, Computational and Applied Mathematics, University of California,

  Feb 9, 12:00 - 13:00

  Auditorium between 2 and 3

  The convergence of artificial intelligence (AI) and applied mathematics offers transformative potential for addressing challenges in scientific computing and control. In this talk, I will highlight my research at the intersection of optimal control and modern AI methods, focusing on the development of scalable, robust, and efficient algorithms to tackle high-dimensional systems with uncertainty.

Feb 2 - Feb 8, 2025

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  A Limiting Model for MEMS with Heterogeneous Dielectric Properties

  Katerina Nik, Assistant Professor, Applied Mathematics and Computational Sciences

  Feb 6, 12:00 - 13:00

  B9, L2, R2325

  Idealized microelectromechanical systems (MEMS) consist of two dielectric plates: a rigid ground plate above which an elastic plate is suspended. The elastic plate deforms due to a Coulomb force induced by a voltage difference applied between the two components. Coating the ground plate with an insulating layer prevents direct contact between the plates.
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  Random Matrix and SYK model

  Prof. Gang Tian

  Feb 4, 16:00 - 17:00

  B1, L3, R3119

  The random matrix theory was started in the 1930s, with notable contributions made by J. Wishart, Baolu Xu and others. During the 1950s of last century, physicist E. Wigner introduced random matrices as a means to simulate the nuclei of heavy atoms, predicting that the spacing between the spectra of heavy atomic nuclei should mirror the spacing between the eigenvalues of random matrices. Many prominent mathematicians also worked on this theory and made very important progress. In this talk, I will begin with some basics on the random matrix theory, then I will discuss some results obtained by my collaborators and myself. Next I will briefly discuss the SYK model, which is now a very active research area in physics. Our objective is to establish a mathematical theory for the SYK model, and I will present some advancements we made as well as some open problems.
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  KAUST International Research Conference on Multi-Grid and Multi-Scale Methods in Computational Science, IMG 2025

  Feb 3 - 5, All day

  Building 19 and 20

  Multigrid Domain Decomposition HPC computational fluid dynamics Computational finance

  The International Conference on Multigrid and Multiscale Methods in Computational Science (IMG) offers a dynamic forum for researchers to share advances in multigrid, multilevel and multiscale computational techniques, as well as their applications across scientific domains.

Jan 26 - Feb 1, 2025

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  Singular Points of Solutions of Hamilton-Jacobi Equations

  Melih Ucer, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Jan 30, 12:00 - 13:00

  B9, L2, R2325

  The Hamilton-Jacobi equation is a prototypical nonlinear first-order PDE which appears in various settings including dynamical systems, optimal control, differential games, etc. where some action/value function solves this PDE. However, these action/value functions are often not smooth on the entire domain, and indeed the Hamilton-Jacobi equation often does not admit globally smooth solutions.
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  An introduction to Virtual Elements in 3D

  Professor Lourenco Beirao da Veiga, University of Milano-Bicocca

  Jan 28, 16:00 - 17:00

  B1, L3, R3119

  The Virtual Element Method (VEM), is a very recent technology introduced in for the discretization of partial differential equations, that has shared a good success in recent years. The VEM can be interpreted as a generalization of the Finite Element Method that allows to use general polygonal and polyhedral meshes, still keeping the same coding complexity and allowing for arbitrary degree of accuracy. The Virtual Element Method makes use of local functions that are not necessarily polynomials and are defined in an implicit way. Nevertheless, by a wise choice of the degrees of freedom and introducing a novel construction of the associated stiffness matrixes, the VEM avoids the explicit integration of such shape functions.
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  NumPDE Workshop: Numerical Analysis of PDEs

  Jan 26, 08:45 - Jan 28, 15:35

  Sunday 26/01 (morning, 8.45-13.30) - Auditorium between Building 2 and Building 3; Sunday 26/01 (afternoon, from 13.45) - Building 9, Room 2322 (Lecture Hall); Monday 27/01 and Tuesday 28/01 - Auditorium between Building 4 and Building 5

  PDEs numerical analysis

  Explore cutting-edge numerical analysis of PDEs, including Finite Element Methods, Virtual Element Methods, and Isogeometric Analysis, with leading experts.

Jan 19 - Jan 25, 2025

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  Parametric Loudspeaker: From Theory To Applications

  Jun Yang, Distinguished Professor, Chinese Academy of Sciences

  Jan 23, 10:00 - 11:00

  B1 L3 R3119

  This talk highlights a comprehensive overview of research and technical advances related to PALs, covering modeling and simulation computation, measurements, signal processing, beamsteering, and their implementation and application.

Jan 12 - Jan 18, 2025

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  Enriching Your CV, Expanding Networks, and Building a Reputation

  Majid Hassanizadeh, Senior Professor, Stuttgart Center for Simulation Science (SIMTECH), Stuttgart University, Germany

  Jan 13, 16:00 - 17:00

  B1, L3, R3119; Zoom link

  Navigating the academic and professional landscape as an early-career researcher requires not only technical expertise but also strategic self-presentation, networking, and reputation management. This lecture explores three critical areas to help PhD students and postdoctoral researchers elevate their professional profiles and create opportunities for growth.
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  Derivation of cross-diffusion models in population dynamics: dichotomy, time-scales, and fast-reaction

  Cinzia Soresina, Montalcini Tenure-Track Assistant Professor, Department of Mathematics, University of Trento, Italy

  Jan 13, 14:30 - 15:30

  B1, L3, R3119

  In population dynamics, cross-diffusion describes the influence of one species on the diffusion of another. A benchmark problem is the cross-diffusion SKT model, proposed in the context of competing species to account for stable inhomogeneous steady states exhibiting spatial segregation.

Dec 22 - Dec 28, 2024

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  SDE, BSDE under Nonlinear Expectation and Fully Nonlinear Path-dependent Parabolic PDE

  Shige Peng, Academician of the Chinese Academy of Sciences, Professor of Mathematics, and Chairman of the Research Center for Mathematics and Interdisciplinary Sciences at Shandong University

  Dec 22, 11:00 - 12:00

  B1, L3, R3119

  In this talk we begin with stochastic differential equations (SDE, or FSDE) and backward stochastic differential equations (BSDE) driven by a d-dimensional G-Brownian motion defined on a nonlinear expectation space. The corresponding BSDE is then a path-solution of the corresponding (deterministic) path-dependent fully nonlinear partial differential equation of second order. This, combined with the powerful deep learning approach, provides an important tool for solving numerical solutions of a system of high dimensional partial differential equations of parabolic type.

Dec 8 - Dec 14, 2024

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  From perfect numbers to Egyptian fraction

  Tianxin Cai

  Dec 12, 16:00 - 17:00

  B5, L5, R5209

  In this talk, the author will discuss several classic number theory problems such as perfect numbers, Waring's problem, Fermat's Last Theorem, Hilbert's 8th problem, Egyptian fractions, etc., and raise some new ideas which are the variety of the classic ones, they are the combination of additive and multiplicative equations. Meanwhile, we raise several interesting problems, new applications or conjectures.
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  A Construction of C^r Conforming Finite Element Spaces in Any Dimension

  Jun Hu

  Dec 12, 12:00 - 13:00

  B1, L3, R3119

  This talk proposes a construction of C r conforming finite element spaces with arbitrary r in any dimension. It is shown that if k≥2 d r+1 the space P k of polynomials of degree not greater than k can be taken as the shape function space of C r finite element spaces in d dimensions. This is the first work on constructing such C r conforming finite elements in any dimension in a unified way.
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  Integral models with nonlocal operators: applications and recent development

  Qiang Du, Fu Foundation Professor of Applied Mathematics at Columbia University

  Dec 10, 16:00 - 17:00

  B1, L3, R3119

  Recent applications and theoretical developments of integral models using nonlocal operators have shown promise as effective alternatives to local models, especially in the presence of singularities and anomalies.

Dec 1 - Dec 7, 2024

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  Causal Recommender Systems

  Xiao-Hua (Andrew) Zhou

  Dec 5, 16:00 - 17:00

  B1, L3, R3119

  In this presentation, I will give an overview of the recommender systems (RS) from causal prospective and discuss my team works on the causal recommendation. First, I introduce the potential outcome framework in recommender system. Second, I provide a brief overview for the debiasing strategies. Then I introduce our woks to address the bias in RS from causal perspective. FinallyI discuss some open problem and future direction in recommender systems.
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  Improving Optimization of Likelihood-based Generative Models with One Line of Code

  Maurizio Filippone, Associate Professor, Statistics

  Dec 5, 12:00 - 13:00

  B9, L2, R2325

  Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images.
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  From Pentamodes to Cable Webs to Masonry Structures

  Graeme Milton

  Dec 5, 10:00 - 11:00

  B1, L3, R3119

  Pentamode materials are a class of materials that are useful for guiding stress. In particular, they have been proposed for acoustic cloaking by guiding stress around objects, and have been physically constructed. A key feature of pentamode materials is that each vertex in the material is the junction of 4 double cone elements.
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  Multilevel sampling and goal-oriented adaptive finite elements for random PDEs and advances in randomized quasi-Monte Carlo

  Yang Liu , Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Dec 4, 16:00 - 18:30

  B2, L5, R5209

  This thesis explores numerical methods for uncertainty quantification in systems governed by random partial differential equations (PDEs) and advances in randomized quasi-Monte Carlo (RQMC) techniques.
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  Monotone Operator methods for proving existence of solutions to Dirichlet-type problems

  Melih Ucer, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Dec 4, 14:15 - 15:15

  B1, L4, R4214

  I will discuss several standard techniques for proving existence of solutions to boundary value problems involving second-order elliptic PDE.

Nov 24 - Nov 30, 2024

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  Mathematics and Sustainability - A Teaching Perspective - Part 2

  Henrik Shahgholian

  Nov 28, 15:00 - 16:00

  B3, L5, R5220

  In this two-part short course, I will present to the participants my recent activities related to teaching mathematics students about sustainability, including introducing and raising awareness of the sustainability topic.
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  LoCoDL: Communication-Efficient Distributed Optimization with Local Training and Compression

  Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

  Nov 27, 12:00 - 13:00

  B9, L3, R3125

  In distributed optimization, and even more in federated learning, communication is the main bottleneck. We introduce LoCoDL, a communication-efficient algorithm that leverages the two techniques of Local training, which reduces the communication frequency, and Compression with a large class of unbiased compressors that includes sparsification and quantization strategies.
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  Constraint Maps and their free boundaries

  Henrik Shahgholian

  Nov 26, 16:00 - 17:00

  Building 1, Level 3, Room 3119

  I shall give an introduction to the topic of constraint maps, which sits on the intersection of free boundaries, and harmonic maps. The talk will start at level of graduate and will also smoothly merge current work that has been developed recently.
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  Mathematics and Sustainability - A Teaching Perspective - Part 1

  Henrik Shahgholian

  Nov 25, 15:00 - 16:00

  B3, L5, R 5220

  In this two-part short course, I will present to the participants my recent activities related to teaching mathematics students about sustainability, including introducing and raising awareness of the sustainability topic.

Nov 17 - Nov 23, 2024

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  Efficient and Structure-Preserving Numerical Schemes for Kohn-Sham Gradient Flow Models

  Xiuping Wang, Ph.D. Student, Applied Mathematics and Computational Sciences

  Nov 20, 09:00 - 10:00

  B4, L5, R5220

  This dissertation presents efficient and structure-preserving numerical schemes for calculating the electronic structure via the Kohn-Sham density functional theory (DFT) and gradient flow models. The efficiency relies on the numerical scheme’s unconditional energy stability, orbital-wise splitting design, and linearity. The structure-preserving property refers to both energy stability and orthonormality-preserving. The corresponding theoretical analysis is rigorously conducted and presented. Various numerical examples are performed to show the schemes’ effectiveness and robustness.
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  Deep Adaptive Sampling for Numerical PDEs

  Prof. Tao Zhou, Chinese Academy of Sciences

  Nov 19, 16:00 - 17:00

  B1, L3, R3119

  We present a deep adaptive sampling method for solving PDEs where deep neural networks are utilized to approximate the solutions.

Jun 9 - Jun 15, 2024

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  On the Natural Gradient Descent

  Prof. Levon Nurbekyan

  Jun 11, 16:00 - 17:00

  KAUST

  gradient methods

  Abstract Numerous problems in scientific computing can be formulated as optimization problems of suitable parametric models over parameter spaces. Neural network and deep learning methods provide unique capabilities for building and optimizing such models, especially in high-dimensional settings. Nevertheless, neural networks and deep learning techniques are often opaque and resistant to precise control of their mathematical properties in terms of architectures, hyperparameters, etc. Consequently, optimizing neural network models can result in a laborious hyperparameter tuning process that

May 26 - Jun 1, 2024

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  A Whole-genome Model of DNA Replication in Human Cells

  Dr. Francisco Berkemeier, University of Cambridge

  May 28, 16:00 - 17:00

  B5 L5 R5209

  genome biological models

  DNA replication is a fundamental cellular process and its precise regulation is crucial for maintaining genomic integrity. Inspired by the mathematical parallelism between DNA replication and the nucleation problem in one-dimensional crystal growth kinetics, we introduce a model that maps whole-genome replication dynamics based on the firing rate profiles of replication origins and fork movement.

May 19 - May 25, 2024

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  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024

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

  May 19, 08:00 - May 30, 17:00

  B2 B3 A0215

  stochastic algorithms statistical learning machine learning

  We are excited to announce the upcoming Stochastic Numerics and Statistical Learning: Theory and Applications Workshop 2024, taking place at KAUST, Auditorium 0215 b/w B4&5, from May 19-30, 2024. Following the highly successful last two years 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 2024 workshop aims to build on the achievements of last year's event, which featured 30 talks, two mini-courses, and two poster sessions, attracting over 150 participants from various universities and research institutes. In 2022 and 2023, 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, CUHK Shenzhen, and Imperial College London, among others.

May 12 - May 18, 2024

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  Hierarchical ensemble-based data assimilation and rare event tracking

  Gaukhar Shaimerdenova, Ph.D. Student, Applied Mathematics and Computational Sciences

  May 15, 10:00 - 12:00

  B4 L5 R5220

  numerical analysis

  This thesis consists of three papers considering, in general, two challenges: the minimization of computational cost in the ensemble Kalman filtering (EnKF) method and the problem of tracking a rare event within the framework of the EnKF.
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  Models, Methods, Applications: HPC in Medicine, Science, Engineering, and AI

  Rolf Krause, Professor, Applied Mathematics and Computational Sciences

  May 14, 16:00 - 17:00

  B3 L5 R5209

  machine learning medicine AI

  Abstract The accurate numerical prediction of complex processes in medicine, science, and engineering requires a well balanced interplay of mathematical models, numerical methods, and scientific software. In this talk, we discuss along different examples from engineering, earth-sciences, and medicine the various challenges in building predictive models. Our focus will be on the algorithmic aspects, i.e. on the numerical methods, -may it be finite elements or neural networks in the context of massive parallelism. We will present in particular parallel preconditioners for non-linear problems
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  Towards trustworthy use of scientific machine-learning in large scale numerical simulations

  Alena Kopanicakova, Researcher, Brown University

  May 13, 09:00 - 10:00

  B9 L3 R3128

  numeric simulations

  Recently, scientific machine learning (SciML) has expanded the capabilities of traditional numerical approaches by simplifying computational modeling and providing cost-effective surrogates. However, SciML models suffer from the absence of explicit error control, a computationally intensive training phase, and a lack of reliability in practice. In this talk, we will take the first steps toward addressing these challenges by exploring two different research directions.

May 5 - May 11, 2024

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  Intracellular "in silico microscopes" - Fully 3D Spatio-Temporal Virus Replication Model Simulations

  Gabriel Wittum, Professor, Applied Mathematics and Computational Sciences

  May 9, 12:00 - 13:00

  B9 L2 H2 H2

  Despite being small and simple structured in comparison to their victims, virus particles have the potential to harm severly and even kill highly developed species such as humans. To face upcoming virus pandemics, detailed quantitative biophysical understanding of intracellular virus replication mechanisms is crucial.
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  Optimization for Deep Learning

  Dr. Konstantin Mischenko, Samsung AI Center, Cambridge, UK

  May 7, 16:00 - 17:00

  B5 L5 R5209

  Deep learning mathematical optimization

  Abstract The field of optimization for machine learning has undergone significant changes in recent years with deep learning models increasing in scale and fine-tuning taking a more prominent role. In this presentation, I will share a perspective on the direction of changes in the field and highlight interesting research directions. I will provide real-world examples of what practitioners want from optimization methods to train deep networks at scale. I will then present my recent work on adaptive methods, such as Adam and Adagrad, and explain how we can estimate the learning rate for these
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  Multigrid Monte Carlo Revisited: Theory and Bayesian Inference

  Professor Robert Scheichl, University of Heidelberg

  May 5, 11:00 - 12:00

  B1 L3 R3119

  Multigrid Monte Carlo

  Abstract The fast simulation of Gaussian random fields plays a pivotal role in many research areas such as Uncertainty Quantification, data science and spatial statistics. In theory, it is a well understood and solved problem, but in practice the efficiency and performance of traditional sampling procedures degenerates quickly when the random field is discretised on a grid with spatial resolution going to zero. Most existing algorithms, such as Cholesky factorisation samplers, do not scale well on large-scale parallel computers. On the other hand, stationary, iterative approaches such as the

Apr 28 - May 4, 2024

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  Non-standard Finite Elements for Eigenvalue Problems

  Linda Alzaben, Ph.D. Student, Applied Mathematics and Computational Sciences

  May 2, 14:00 - 15:00

  B1 L2 R2202

  Numerical approximation of partial differential equations involves parameter dependencies from problem formulation and numerical methods. We focus on two areas: least-squares finite element method with linear elasticity, studying its dependence on the Lamé parameter, and the Virtual Element Method, known for handling complex geometries where the stabilization parameter is analyzed.
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  Statistical Deep-Learning for Spatiotemporal Extremes

  Raphaël Huser, Associate Professor, Statistics

  May 2, 12:00 - 13:00

  B9 L2 H2 H2

  Rare, low-probability events often lead to the biggest impacts. Therefore, the development of statistical approaches for modeling, predicting and quantifying environmental risks associated with natural hazards is of utmost importance. In this seminar, I will show how statistical deep-learning methods can help solve challenges that arise when modeling complex and massive spatiotemporal extremes data.
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  Virtual Element Approximation of Eigenvalue Problems

  Prof. Francesca Gardini, Università di Pavia

  Apr 30, 16:00 - 17:00

  B1 L3 R3119

  Virtual Element Method Elgenvalue FEMSimulation PDE

  We will discuss the solution of eigenvalue problems associated with partial differential equations (PDE)s that can be written in the generalised form Ax = λMx, where the matrices A and/or M may depend on a scalar parameter. Parameter dependent matrices occur frequently when stabilised formulations are used for the numerical approximation of PDEs. With the help of classical numerical examples we will show that the presence of one (or both) parameters can produce unexpected results.
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  A Free Boundary Problem for Accretive Growth

  Katerina Nik, Postdoc, Applied Mathematics and Modelling Group, University of Vienna

  Apr 28, 09:00 - 10:00

  B9 L3 R3128

  Growth is a fundamental process in biological systems and various technological applications, including epitaxial deposition and additive manufacturing. The interaction between growth and mechanics in deformable bodies leads to a wealth of very challenging mathematical questions. I will give a short overview of the key concepts of morphoelasticity, namely, the theory of elastic deformations in growing bodies.

Apr 14 - Apr 20, 2024

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  Traveling wave solutions of Reaction-Diffusion equation in Biological mathematics

  Hoyoun Kim, Postdoctoral Research Fellow, Applied Mathematics and Computational Sciences

  Apr 18, 12:00 - 13:00

  B9 L2 H2 H2

  Traveling wave solutions of reaction-diffusion systems have been studied to explain wave propagation phenomena in biological organisms.