Events

Nov 27 - Dec 3, 2022

• 

  A unifying partially-interpretable framework for neural network-based extreme quantile regression

  Raphaël Huser, Associate Professor, Statistics

  Nov 29, 12:00 - 13:00

  B9 L2 R2322

  Artificial Neural Network quantile regression

  In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neural networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data.
• 

  On the collapse mechanism of Wave Maps

  Prof. Manoussos Grillakis, Departments of Mathematics, University of Maryland

  Nov 27, 13:00 - 15:00

  B1 L4 R4214

  Partial Differential Equations

  The Wave Map system describes the evolution of waves constrained on a (Riemannian) manifold. For the 2 + 1 dimensional problem, when the target manifold is a sphere, the solution collapses in finite time. The Analysis is due to the pioneering work of Merle, Paphael and Rodnianski. Motivated by their work I will present a somewhat novel approach of the collapsing mechanism which is based on a view of the equations as a nonlinear gauge system. This is joint work with Dan Geba.

Nov 20 - Nov 26, 2022

• 

  Sharp Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces

  Assistant Professor Jonathan Siegel, Texas A and M University

  Nov 22, 15:30 - 17:00

  B1 L3 R3119

  ReLU Neural Network Sobolev Spaces

  Sobolev spaces are centrally important objects in PDE theory. Consequently, to understand how deep neural networks can be used to numerically solve PDEs a necessary first step is to determine now efficiently they can approximate Sobolev functions. In this talk we consider this problem for deep ReLU neural networks, which are the most important class of neural networks in practical applications.
• 

  Lipschitz learning and the infinity-Laplacian

  Miguel Urbano, Professor, Applied Mathematics and Computational Science

  Nov 22, 12:00 - 13:00

  B9 L2 R2322

  Lipschitz additive nonlinear uncertainty infinity Laplacian

  Infinity-harmonic functions have recently found application in Semi-Supervised Learning, in the context of the so-called Lipschitz Learning. With this application in mind, we will discuss the Lipschitz extension problem, its solution via MacShane-Whitney extensions and its several drawbacks, leading to the notion of AMLE (Absolutely Minimising Lipschitz Extension).
• 

  On training algorithms for neural networks

  Jinchao Xu, Professor, Applied Mathematics and Computational Science

  Nov 21, 12:00 - 13:00

  B9 L2 R2322 H1

  Deep learning deep neural networks

  In this talk, I will first give a convergence analysis of gradient descent (GD) method for training neural networks by relating them with finite element method. I will then present some acceleration techniques for GD method and also give some alternative training algorithms

Nov 13 - Nov 19, 2022

• 

  Functional Differential Equations in Models of Collective Behavior - Graduate Seminar

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

  Nov 15, 12:00 - 13:00

  B9 L2 R2322

  Functional Differential Equation

  The talk will give an overview of recent results for models of collective behavior governed by functional differential equations. It will focus on models of interacting agents with applications in biology (flocking, swarming), social sciences (opinion formation) and engineering (swarm robotics), where latency (delay) plays a significant role.

Nov 6 - Nov 12, 2022

• 

  Introduction to Computer Simulation - A short course II - Part 3

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 10, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Introduction to Computer Simulation - A short course II - Part 2

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 9, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Parallel multilevel solvers for virtual element discretizations of saddle point problems

  Prof. Simone Scacchi, Associate Professor of Numerical Analysis at the Department of Mathematics of the University of Milan

  Nov 8, 15:30 - 17:00

  B1 L3 R3119

  Virtual Element Method

  In this seminar, we will present our work on Virtual Element Method (VEM) approximations. The Virtual Element Method is a recent numerical technique for solving partial differential equations on computational grids constituted by polygonal or polyhedral elements of very general shape. This work aims to develop effective linear solvers for general order VEM approximations of three-dimensional scalar elliptic equations in mixed form and Stokes equations. To this end, we consider block algebraic multigrid preconditioners and balancing domain decomposition by constraints (BDDC) preconditioners. The latter allows us to use conjugate gradient iterations, albeit the algebraic linear systems arising from the discretization of the differential problems are indefinite, ill-conditioned, and of saddle point nature.
• 

  Modeling water waves: from tsunamis to the kitchen sink

  David Ketcheson, Professor, Applied Mathematics and Computational Science

  Nov 8, 12:00 - 13:00

  B9 L2 R2322

  water waves Tsunamis Statistical Modelling

  Surface water waves are a physically important phenomenon with which we all have some experience. They are also surprisingly complex and interesting from a mathematical perspective. I will discuss two recent projects in water wave modeling. The first deals with ocean waves, such as tsunamis, passing over the continental slope. It has long been known that the amplification of such waves is greater than what the traditional transmission coefficient would predict.
• 

  Introduction to Computer Simulation - A short course II - Part 1

  Prof. Michal Mankowski, Assistant Professor of Operations Research, Erasmus University Rotterdam, Netherlands

  Nov 8, 10:00 - 11:30

  B1 L3 R3119

  The aim of this course is to familiarize the students with the usage of Computer Simulation tools for complex problems. The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used in industry and academia. The basic concepts of Discrete Event Simulation will be introduced along with the reliable methods of random variate generation and variance reduction. Later in the course, the concept of simulation-based optimization and output analysis will be discussed. The example of simulation (and optimization) applied to design an optimal organ allocation policy in the US will be discussed.
• 

  Numerical approximations of Mean-Field-Games

  Serikbolsyn Duisembay, Ph.D. Student, Applied Mathematics and Computational Science

  Nov 7, 14:00 - 16:00

  B1 L2 R2202

  mean-field games

  In this thesis, we present three projects. First, we investigate the numerical approximation of Hamilton-Jacobi equations with the Caputo time-fractional derivative. We introduce an explicit in time discretization of the Caputo derivative and a finite-difference scheme for the approximation of the Hamiltonian. We show that the approximation scheme is stable under an appropriate condition on the discretization parameters and converges to the unique viscosity solution of the Hamilton-Jacobi equation.

Oct 30 - Nov 5, 2022

• 

  Numerical approximation of McKean-Vlasov SDEs via Stochastic Gradient Descent

  Prof. Gonçalo dos Reis, School of Mathematics, University of Edinburgh

  Nov 1, 15:30 - 17:00

  B1 L3 R3119

  stochastic gradient descent McKean-Vlasov SDEs

  We propose a novel approach of numerically approximate McKean-Vlasov SDEs that avoids the usual interacting particle approximation and Propagation of Chaos results altogether.

Oct 23 - Oct 29, 2022

• 

  On Some PDEs with singular data

  Prof. Mohammed Kbiri Alaoui, King Khalid University

  Oct 25, 15:30 - 17:00

  B1 L3 R3119

  PDEs singular data

  The aim of this talk is to investigate the following non local p-Laplacian problem with data a bounded Radon measure.
• 

  A journey in applied complexity: pioneering record-performing technologies in clean energy harvesting, machine vision, and smart materials - Public Colloquium

  Andrea Fratalocchi, Professor, Electrical and Computer Engineering

  Oct 25, 14:00 - 16:00

  B9 L2 R2322

  Applied complexity artificial intelligence information security biomedical applications

  Applied Complexity aims to understand the physical origin of these behaviors and transform them into sustainable technologies that tackle global problems of global interest. These range from energy harvesting to clean water production, the design of smart materials, biomedical applications, information security, artificial intelligence, and global warming. In this talk, I will summarize my group's recent research, discussing present results and future challenges of Applied complexity both as a science and engineering.
• 

  Mean-Field Games on Networks and Wardrop Equilibria - Graduate Seminar

  Ricardo De Lima Ribeiro, Research Specialist, CEMSE, KAUST

  Oct 25, 12:00 - 13:00

  B9 L2 R2322

  mean-field games

  Models for flows on networks arise in the study of traffic and pedestrian crowds. These models encode congestion effects, the behavior and preferences of agents, such as aversion to crowds and their attempts to minimize travel time. We will present the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary mean-field game.

Oct 16 - Oct 22, 2022

• 

  Inference for Longitudinal Data After Adaptive Sampling

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 20, 15:00 - 16:00

  B9 L2 H2

  Reinforcement Learning

  In this work, we proved statistical inference for the common Z-estimator based on adaptively sampled data. Adaptive sampling methods, such as reinforcement learning (RL) and bandit algorithms, are increasingly used for the real-time personalization of interventions in digital applications like mobile health and education. As a result, there is a need to be able to use the resulting adaptively collected user data to address a variety of inferential questions, including questions about time-varying causal effects.
• 

  We used Reinforcement Learning; but did it work?

  Prof. Susan Murphy, Statistics and Computer Science and Radcliffe Alumnae Professor at the Radcliffe Institute, Harvard University

  Oct 19, 16:00 - 17:00

  B9 L2 H2

  Reinforcement Learning Digital Behavioral Health

  Reinforcement Learning provides an attractive suite of online learning methods for personalizing interventions in Digital Behavioral Health. However, after a reinforcement learning algorithm has been run in a clinical study, how do we assess whether personalization occurred? We might find users for whom it appears that the algorithm has indeed learned in which contexts the user is more responsive to a particular intervention. But could this have happened completely by chance? We discuss some first approaches to addressing these questions.
• 

  Gradient Estimates for weak solutions of Elliptic PDE's - Colloquium

  Giuseppe Di Fazio, Professor, University of Catania

  Oct 18, 15:30 - 17:00

  B1 L3 R3119

  Elliptic PDE

  In the Seminar, we exploit what is the heart of the technique to show gradient estimates allowing the function ? to belong to very general function spaces. Our technique is very flexible and allows us to show the existence, uniqueness, and well-posedness of the Dirichlet problem in several classes.
• 

  Distributed multi-GPU Algorithms for Hierarchical Matrices

  George Turkiyyah, Research Professor, Applied Mathematics and Computational Science

  Oct 18, 12:00 - 13:00

  B9 L2 R2322

  GPU Algorithms hierarchical matrices

  In this talk, we show that, besides their optimal O(N) algorithmic complexity, hierarchical matrix operations also benefit from parallel scalability on distributed machines with extremely large core counts. In particular, we describe high-performance, distributed-memory, GPU-accelerated algorithms for matrix-vector multiplication and other operations on hierarchical matrices in the H^2 format.
• 

  Recent advances in mixed finite element approximation for poroelasticity

  Arbaz Khan, Assistant Professor, Department of Mathematics, Indian Institute of Technology (IIT)

  Oct 18, 11:00 - 12:00

  B1 L3 R3119

  parallel computing Numerical Solution of Partial Differential Equations stochastic differential equations

  This talk discusses the non-conforming approximation of Biot's consolidation model. In the first part of the talk, we discuss posteriori error estimators for locking-free mixed finite element approximation of Biot’s consolidation model. In the second part of the talk, we discuss a novel locking-free stochastic Galerkin mixed finite element method for the Biot consolidation model with uncertain Young’s modulus and hydraulic conductivity field.

Oct 9 - Oct 15, 2022

• 

  A Fast Normalized Cut with Automated Constraints via Graph Coarsening

  Prof. Young Ju Lee, Department of Mathematics, Texas State University

  Oct 11, 15:30 - 17:00

  B1 L3 R3119

  Normalized Cut Graph Coarsening

  We present a new constrained K-way data clustering algorithm based on Normalized Cut by Shi and Malik. A novelty in our algorithm lies in selecting constraints automatically from the data by using a multiscale coarsening algorithm.
• 

  Finite element approximation of parameter dependent eigenvalue problems

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

  Oct 11, 12:00 - 13:00

  B9 L2 R2322

  Finite element approximation of parameter dependent eigenvalue problems

  Eigenvalue problems arising from partial differential equations are used to model several applications in science and engineering, ranging from vibrations of structures, industrial microwaves, photonic crystals, and waveguides, to particle accelerators.

Oct 2 - Oct 8, 2022

• 

  Geometry and Computational Design of Equilibrium Structures

  Oct 4, 15:30 - 17:00

  B1 L3 R3119

  Geometry computational design

  Freeform structures play a prominent role in contemporary architecture. In order to stay within reasonable cost limits, computational shape design has to incorporate aspects of structural analysis and fabrication constraints. The talk discusses solutions to important problems in this area. They concern the design of polyhedral surfaces with nearly rectangular faces, polyhedral surfaces in static equilibrium, the smoothest visual appearance of polyhedral surfaces and the closely related problem of finding material-minimizing forms and structures. From a methodology perspective, there is an interplay of geometry, mechanics and optimization. Classical subjects such as isotropic geometry, a simple Cayley-Klein geometry, play a role as well as most recent developments in discrete differential geometry. We also show how practical requirements have led to new results and open problems in geometry.
• 

  Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining - 2022-10-04

  Mikhail Moshkov, Professor, Applied Mathematics and Computational Science

  Oct 4, 12:00 - 13:00

  B9 L2 R2322

  Dynamic programming combinatorial optimization data mining

  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.
• 

  A new class of random field models for data on networks

  David Bolin, Professor, Statistics

  Oct 3, 12:00 - 13:00

  B9 L2 R2322 H1

  applied statistics machine learning Gaussian processes

  Random fields are popular models in statistics and machine learning for spatially dependent data on Euclidian domains. However, in many applications, data is observed on non-Euclidian domains such as street networks. In this case, it is much more difficult to construct valid random field models. In this talk, we discuss some recent approaches to modeling data in this setting, and in particular define a new class of Gaussian processes on compact metric graphs.

Sep 25 - Oct 1, 2022

• 

  Modeling and Numerics of Sorption Kinetics

  Giovanni Russo, Professor, Department of Mathematics and Computer Science, University of Catania

  Sep 27, 15:30 - 17:00

  B1 L3 R3119

  modeling sorption kinetics

  An efficient method is proposed for the numerical solution of the Stokes equations in a domain with a moving bubble and two techniques for the treatment of the boundary conditions are adopted and then compared. The treatment of diffusion of surfactants (anions and cations) in presence of an oscillating bubble is an interesting interdisciplinary problem, with applications to chemistry and biology.
• 

  Deep Learning and Scientific Computing

  Jinchao Xu, Professor, Applied Mathematics and Computational Science

  Sep 27, 12:00 - 13:00

  B9 L2 R2322

  Deep learning scientific computing

  In this talk, I will first give an elementary introduction to basic deep learning models and training algorithms from a scientific computing viewpoint. Using image classification as an example, I will try to give mathematical explanations of why and how some popular deep learning models such as convolutional neural network (CNN) work. Most of the talk will be assessable to an audience who have basic knowledge of calculus and matrix. Toward the end of the talk, I will touch upon some advanced topics to demonstrate the potential of new mathematical insights for helping understand and improve the efficiency of deep learning technologies.

Sep 18 - Sep 24, 2022

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  Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting

  Daniel Paulin, Assistant Professor, School of Mathematics, University of Edinburgh.

  Sep 20, 15:30 - 17:00

  B1 L3 R3119

  Monte Carlo MCMC

  In this paper, we propose a detailed theoretical study of one of these algorithms known as the split Gibbs sampler. Under regularity conditions, we establish explicit convergence rates for this scheme using Ricci curvature and coupling ideas. We support our theory with numerical illustrations.
• 

  Waves Functional Materials: From theory to applications

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Sep 20, 12:00 - 13:00

  B9 L2 R2322

  waves Wave functional materials Applications

  No abstract is available.

Sep 11 - Sep 17, 2022

• 

  On the resolution of a theoretical question related to the nature of local training in federated learning

  Peter Richtarik, Professor, Computer Science

  Sep 13, 15:30 - 17:00

  B1 L3 R3119

  machine learning mathematical optimization communications algorithms

  In this talk, I will explain the problem, its solution, and some subsequent work generalizing, extending and improving the ProxSkip method in various ways. We study distributed optimization methods based on the local training (LT) paradigm - achieving improved communication efficiency by performing richer local gradient-based training on the clients before parameter averaging - which is of key importance in federated learning. Looking back at the progress of the field in the last decade, we identify 5 generations of LT methods: 1) heuristic, 2) homogeneous, 3) sublinear, 4) linear, and 5) accelerated. The 5th generation, initiated by the ProxSkip method of Mishchenko et al (2022) and its analysis, is characterized by the first theoretical confirmation that LT is a communication acceleration mechanism.
• 

  Solvers for Coupled Problems in Porous Media Science

  Arne Naegel

  Sep 13, 12:00 - 13:00

  B9 L2 R2322

  applied statistics

  No abstract is available.

Sep 4 - Sep 10, 2022

• 

  Pore-Scale Two-Phase Flows: Modeling, Algorithms, and Applications

  Sep 6, 15:30 - 17:00

  B1 L3 R3119

  Multiphase flows

  Abstract Two or multiple phases in fluid mixture commonly occur in petroleum industry, where oil, gas and water are often produced and transported together. Petroleum reservoir engineers spent great efforts in drainage problems arising from the development and production of oil and gas reservoirs so as to obtain a high economic recovery, by developing, conducting, and interpolating the simulation of subsurface flows of reservoir fluids, including water, hydrocarbon, CO2, H2S for example in porous geological formation. Field-scale or Darcy-scale simulation has conventionally and routinely used
• 

  Low Rank Everywhere (Almost)

  David Keyes, Professor, Applied Mathematics and Computational Science

  Sep 6, 12:00 - 13:00

  B9 L2 R2322

  computational science low rank hierarchical low rank data sparsity

  Tile low-rank and hierarchical low-rank matrices can exploit the data sparsity that is discoverable all across computational science. We illustrate in large-scale applications and hybridize with similarly motivated mixed precision representations while featuring ECRC research in progress with many collaborators.

Aug 28 - Sep 3, 2022

• 

  Emergence of transportation networks as a self-regulated process

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

  Aug 30, 15:30 - 17:00

  B1 L3 R3119

  random process transportation networks

  In this talk, we shall explain how transportation networks emerge as a self-regulating process, with a particular focus on applications in biology (leaf venation in plants, neuronal networks in animals). We start by introducing a purely diffusive model with tensor-valued diffusivity, derived as a gradient flow of a broad class of entropy dissipations. The introduction of a prescribed electric potential leads to the Fokker-Planck equation. We show that with quadratic entropy density modeling Joule heating, the model is convex with respect to the diffusivity tensor.
• 

  Introduction to AMCS / STAT Graduate Seminar 398

  Gabriel Wittum, Professor (former), Applied Mathematics and Computational Science

  Aug 30, 12:00 - 13:00

  B9 L2 R2322

  In this section, prof. Gabriel will be introducing the graduate seminar and its requirements.

Jul 3 - Jul 9, 2022

• 

  Mean-Field Games on Networks and Wardrop Equilibria - PhD Dissertation Defense

  Fatimah H. Al Saleh, PhD Student, Applied Mathematics and Computational Sciences, KAUST, Saudi Arabia

  Jul 6, 10:00 - 12:00

  KAUST

  This thesis consists of three main parts. In the first part, we discuss first-order stationary mean-field games (MFGs) on networks. In the second part, we discuss the Wardrop equilibrium model on networks with flow-dependent costs and its connection with stationary MFGs. First, we build the Wardrop model on networks. Second, we show how to convert the MFG model into a Wardrop model. Next, we recover the MFG solution from the Wardrop solution. Finally, we study the calibration of MFGs with Wardrop travel cost problems. In the third part, we explain the algorithm for solving the algebraic system associated with the MFG numerically, then, we present some examples and numerical results.

May 29 - Jun 4, 2022

• 

  Stochastic differential games, network of networks and the crowd evacuation problem

  Dr. Julian Barreiro Gomez, Center on Stability, Instability, and Turbulence (SITE) at the New York University in Abu Dhabi

  Jun 1, 16:00 - 17:00

  B1 L4 R4102

  In this talk, we present a class of stochastic differential games that can incorporate the distribution of the variables of interest (e.g., the system states and/or decision-makers' actions) into the strategic-interaction problem. We motivate the use of this type of differential games in networked large-scale applications that cover a high variety of engineering systems. In particular, we focus on the crowd evacuation problem. First, we only consider local aggregated congestion terms that penalize the magnitude of the decision-makers’ strategies allowing us to avoid the formation of congestion. Second, we consider both local and global aggregated congestion terms to perform crowd aversion during the evacuation procedure. We present some numerical results and few future directions, e.g., the case where decision-makers do not have prior knowledge about the geometry of the structure to be evacuated neither the existing obstacles.
• 

  Network processing in the NIC: the case for specialized hardware architectures

  Prof. Giuseppe Bianchi, University of Roma Tor Vergata, Italy

  May 29, 12:00 - 13:00

  B9 Hall R2325

  Network specialized hardware architectures

  In the last decade, the networking research community has significantly fueled the network softwarization and virtualization trend. Network processing tasks, originally performed by dedicated hardware appliances, were converted into software components running on commodity hardware, and deployed in relevant cloud infrastructures (central and/or edge).

May 22 - May 28, 2022

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  Mathematical Problems in Deep Learning

  Prof. Hailiang Liu, Department of Mathematics, Iowa State University, USA

  May 25, 15:00 - 16:00

  B1 L4 R4102

  I shall present some mathematical problems encountered in deep learning models. The results include optimal control of selection dynamics for deep neural networks, and gradient methods adaptive with energy. Some of the computational questions that will be addressed have a more general interest in engineering and sciences.

May 15 - May 21, 2022

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  On the Precipice of a Global Transition Toward Electric Vehicles

  Peter Rawlinson, the Chief Executive Officer and Chief Technology Officer of Lucid

  May 19, 14:30 - 15:30

  B20 L2 R2011

  Global Transition Toward Electric Vehicles Revolutionary technology

  In this wide-ranging conversation with Peter Rawlinson, Lucid Motors’ CEO and CTO, he will discuss why he believes the world is on the precipice of a global transition toward electric vehicles, and how Lucid’s revolutionary technology and design will be at the forefront of one of the most significant transformations of our time.
• 

  Stochastic Numerics and Statistical Learning: Theory and Applications Workshop

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

  May 15, 08:00 - May 28, 17:00

  KAUST Campus

  This scientific meeting will concentrate on stochastic algorithms and their rigorous numerical analysis for various problems, including statistical learning, optimization, and approximation. Stochastic algorithms are valuable tools when addressing challenging computational problems.

May 8 - May 14, 2022

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  Non-matching methods for the simulation of multi-dimensional and multi-physics coupled problems

  Prof. Luca Heltai, Applied Mathematics, International School for Advanced Studies

  May 12, 15:00 - 16:00

  B1 L4 R4102

  Real life multi-phase and multi-physics problems coupled across different scales present outstanding challenges, whose practical resolution often require unconventional numerical methods.
• 

  An Introduction to the Boltzmann equation - Part 3

  Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 12, 14:00 - 16:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
• 

  An Introduction to the Boltzmann equation - Part 2

  Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 11, 15:00 - 17:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.
• 

  How to Use Mathematics to Predict Cancer Patient Responses to Immuno-therapy

  Prof. Helen Moore, MD-Pulmonary Systems Medicine Department, University of Florida, USA

  May 10, 17:00 - 18:00

  KAUST

  How to Use Mathematics to Predict Cancer Patient Responses to Immuno-therapy.
• 

  An Introduction to the Boltzmann equation - Part 1

  Prof. Daniela Tonon, Department of Mathematics, University of Padova, Italy

  May 10, 14:00 - 16:00

  KAUST

  In this course, we introduce the Boltzmann equation, i.e. the equation that describes the behavior of rarefied gases at a mesoscopic scale. This scale can be considered as in between the microscopic scale (where the gas is described as a set of a large number of particles) and the macroscopic one (where the gas is described as a continuum fluid). Starting from the classical free transport equation, we will describe the crucial role of the collisional operator that can be deduced from physical assumptions. In particular, we will focus on the formal derivation of the Boltzmann equation and on the techniques used to cope with its particular, highly singular, collisional operator, in the study of the Cauchy problem. We will conclude with the study of the Boltzmann equation in the more physically relevant case of bounded domains, considering several different boundary conditions such as in flow, specular reflection, bounce-back reflection and diffuse boundary conditions.

Apr 24 - Apr 30, 2022

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  Geometry for computational design and fabrication

  Apr 28, 12:00 - 13:00

  B9 L2 H2

  Geometry plays an important role in the design and fabrication of so-called freeform shapes. This talk will illustrate the fruitful interplay between theory and applications in this area. The focus will be on discrete differential geometry and on applications in architecture and fabrication-aware design.
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  Variational Inference for Cutting Feedback in Misspecified Models

  Prof. David Nott, Department of Statistics and Data Science, National University of Singapore

  Apr 27, 10:00 - 11:00

  KAUST

  Bayesian analyses combine information represented by different terms in a joint Bayesian model. When one or more of the terms is misspecified, it can be helpful to restrict the use of information from suspect model components to modify posterior inference.
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  Applied Optimization: from Energy Systems to Trajectory Planning

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

  Apr 24, 14:00 - 15:00

  B9 L2 H2

  This talk focus on advanced modeling and optimization techniques to solve complex problems in the scheduling and planning of processes, energy systems, and water systems. Modeling and optimization techniques integrated with other disciplines have a paramount role in supporting the energy transition planning and the water-energy nexus. Specifically, optimizing the integration of renewable energy sources and new energy carriers in the energy mix, increasing the efficiency of existing and novel systems (enterprise-wide, industry, buildings, supply chains), and supporting decarbonization in industry. First, we describe canonical optimization formulations and show their flexibility and capabilities to address complex optimization problems. Examples of applications include process synthesis, production scheduling, short-term hydro scheduling, unit commitment, and even the transfer of human resources within large organizations. Then we focus on applying stochastic optimization approaches to address a virtual power plant's self-scheduling and market involvement, and finally on applying advanced optimization strategies for trajectory planning of underwater vehicles in uncertain and transient flow fields.