Tuesday, October 04, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
Contact Person
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.
Tuesday, October 04, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
Contact Person
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.
Monday, October 03, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322, Hall 1
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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.
Giovanni Russo, Professor, Department of Mathematics and Computer Science, University of Catania
Tuesday, September 27, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
Contact Person
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.
Tuesday, September 27, 2022, 12:00
- 13:00
Building 9, level 2, Room 2322
Contact Person
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.
Daniel Paulin, Assistant Professor, School of Mathematics, University of Edinburgh.
Tuesday, September 20, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119 or https://kaust.zoom.us/j/91053275355
Contact Person
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.
Tuesday, September 13, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
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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.
Tuesday, September 06, 2022, 12:00
- 13:00
Building 9, Level 2, Room 2322
Contact Person
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.
Tuesday, August 30, 2022, 15:30
- 17:00
Building 1, Level 3, Room 3119
Contact Person
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.
Fatimah H. Al Saleh, PhD Student, Applied Mathematics and Computational Sciences, KAUST, Saudi Arabia
Wednesday, July 06, 2022, 10:00
- 12:00
https://kaust.zoom.us/j/92512099536
Contact Person
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.
Dr. Julian Barreiro Gomez, Center on Stability, Instability, and Turbulence (SITE) at the New York University in Abu Dhabi
Wednesday, June 01, 2022, 16:00
- 17:00
Building 1, Level 4, Room 4102
Contact Person
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.
Prof. Giuseppe Bianchi, University of Roma Tor Vergata, Italy
Sunday, May 29, 2022, 12:00
- 13:00
Building 9, Lecture Hall, Room 2325
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).
Prof. Hailiang Liu, Department of Mathematics, Iowa State University, USA
Wednesday, May 25, 2022, 15:00
- 16:00
Building 1, Level 4, Room 4102, https://kaust.zoom.us/j/96443244183
Contact Person
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.
Peter Rawlinson, the Chief Executive Officer and Chief Technology Officer of Lucid
Thursday, May 19, 2022, 14:30
- 15:30
Building 20, Level 2, Room 2011 (The Rehearsal Room)
Contact Person
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 PI Professor Raul Tempone (Chair) and Computational Probability PI Professor Ajay Jasra (Co-Chair)
Sunday, May 15, 2022, 08:00
- 17:00
KAUST Campus
Contact Person
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.
Prof. Luca Heltai, Applied Mathematics, International School for Advanced Studies
Thursday, May 12, 2022, 15:00
- 16:00
Building 1, Level 4, Room 4102
Contact Person
Real life multi-phase and multi-physics problems coupled across different scales present outstanding challenges, whose practical resolution often require unconventional numerical methods.
Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy
Thursday, May 12, 2022, 14:00
- 16:00
https://kaust.zoom.us/j/92447974621
Contact Person
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.
Prof Daniela Tonon, Department of Mathematics, University of Padova, Italy
Wednesday, May 11, 2022, 15:00
- 17:00
https://kaust.zoom.us/j/92305928731
Contact Person
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.
Prof. Helen Moore, MD-Pulmonary Systems Medicine Department, University of Florida, USA
Tuesday, May 10, 2022, 17:00
- 18:00
https://kaust.zoom.us/j/8570786729
Contact Person
How to Use Mathematics to Predict Cancer Patient Responses to Immuno-therapy.