Prof. Francesca Gardini, Università di Pavia
Tuesday, April 30, 2024, 16:00
- 17:00
Building 1, Level 3, Room 3119
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.
Prof. Edgard Pimentel
Tuesday, March 26, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5220
Hessian-dependent functionals play a pivotal role in a wide latitude of problems in mathematics. Arising in the context of differential geometry and probability theory, this class of problems find applications in the mechanics of deformable media (mostly in elasticity theory) and the modelling of slow viscous fluids. We study such functionals from three distinct perspectives.
Prof. Silvia Bertoluzza
Tuesday, March 05, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5209
We present a theoretical analysis of the Weak Adversarial Networks (WAN) method, recently proposed in [1, 2], as a method for approximating the solution of partial differential equations in high dimensions and tested in the framework of inverse problems. In a very general abstract framework.
Prof. Christof Schmidhuber, ZHAW School of Engineering
Tuesday, February 27, 2024, 16:00
- 17:00
Building 9, Level 2, Room 2322
Contact Person
Analogies between financial markets and critical phenomena have long been observed empirically. So far, no convincing theory has emerged that can explain these empirical observations. Here, we take a step towards such a theory by modeling financial markets as a lattice gas.
Prof. Dr. Victorita Dolean, Mathematics and Computer Science, Scientific Computing, TU Eindhoven
Tuesday, February 06, 2024, 16:00
- 17:00
Building 2, Level 5, Room 5220
Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.
Prof. Zhiming Chen, Academy of mathematics and Systems Science, Chinese Academy of Sciences
Wednesday, January 24, 2024, 14:30
- 16:00
Building 4, Level 5, Room 5220
In this short course, we will introduce some elements in deriving the hp a posteriori error estimate for a high-order unfitted finite element method for elliptic interface problems. The key ingredient is an hp domain inverse estimate, which allows us to prove a sharp lower bound of the hp a posteriori error estimator.
Prof. Mohamed Abdelfattah, Electrical and Computer Engineering Department at Cornell University
Sunday, December 17, 2023, 14:00
- 15:30
Building 2, Level 5, Room 5209
Contact Person
Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.
Prof. Alexander Ostermann
Sunday, December 03, 2023, 16:00
- 17:00
Building 2, Level 5, Room 5220
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Coffee Time: 15:30 - 16:00. Splitting methods are a well-established tool for numerically integrating time-dependent partial differential equations. These methods split the vector field into disjoint components, which are integrated separately using an appropriate time step. The individual flows are then combined to obtain the desired numerical approximation.
Catalina Albornoz, Quantum Community Manager at Xanadu
Tuesday, October 31, 2023, 15:30
- 17:00
KAUST
Contact Person
Xanadu is a Canadian quantum computing company with the mission to build quantum computers that are useful and available to people everywhere. Xanadu is one of the world’s leading quantum hardware and software companies and also leads the development of PennyLane, an open-source software library for quantum computing and application development.
Prof.Gustavo Alonso, Computer Science, ETH Zurich
Monday, March 13, 2023, 12:00
- 13:00
Building 9, Level 2, Room 2325, Hall 2
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In this talk I will discuss the shift towards hardware acceleration and show with several examples from industry and from research the large role that FPGAs are playing. I will hypothesize that we are in a new era where most of the established assumptions, rules of thumb, and accumulated wisdom about many aspects of computation in general and of data processing in particular no longer hold and need to be revisited.
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.
Monday, June 20, 2022, 11:00
- 13:00
Building 9, Level 4, Room 4223
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Scientific applications from diverse sources rely on dense matrix operations. These operations arise in: Schur complements, integral equations, covariances in spatial statistics, ridge regression, radial basis functions from unstructured meshes, and kernel matrices from machine learning, among others. This thesis demonstrates how to extend the problem sizes that may be treated and reduce their execution time. Sometimes, even forming the dense matrix can be a bottleneck – in computation or storage.
speakers from KAUST, Birmingham, Graz, Utrecht, Stuttgart, Frankfurt, Buffalo, Linz, Weissach, Lugano, Kaliningrad, Heidelberg, State College, Philadelphia, Torino, Riyadh
Monday, March 21, 2022, 09:00
- 17:30
Building 3, Level 5, Room 5209
Contact Person

The workshop provides a forum for researchers to present and discuss recent progress in modelling and simula

Gabriel Ghinita, Associate Professor, University of Massachusetts, Boston
Sunday, November 28, 2021, 12:00
- 13:00
B9, L2, R2322
Contact Person
Skyline computation is an increasingly popular query, with broad applicability to many domains. Given the trend to outsource databases, and due to the sensitive nature of the data (e.g., in healthcare), it is essential to evaluate skylines on encrypted datasets.
Jinchao Xu, Affiliate Professor of Information Sciences and Technology, Penn State University
Wednesday, October 13, 2021, 09:00
- 10:00
Building 9, level 2, Room # 2322
Contact Person
I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.
Jinchao Xu, Affiliate Professor of Information Sciences and Technology, Penn State University
Tuesday, October 12, 2021, 09:00
- 10:00
BW BUILDING 4 AND 5 Level: 0 Room: AUDITORIUM 0215
Contact Person
I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.
Jinchao Xu, Affiliate Professor of Information Sciences and Technology, Penn State University
Monday, October 11, 2021, 09:00
- 10:00
BW BUILDING 4 AND 5 Level: 0 Room: AUDITORIUM 0215
Contact Person
I will give a self-contained introduction to the theory of the neural network function class and its application to image classification and numerical solution of partial differential equations.
Bilel Hadri, Computational Scientist, Supercomputing Lab, KAUST
Friday, July 02, 2021, 14:00
- 18:00
ISC21 (virtual), Frankfurt, Germany (Time CET)
Contact Person

Abstract

With the hardware technology scaling and the trend on heterogeneous chip design, the exis

Piotr Luszczek, Research Assistant Professor, University of Tennessee
Monday, March 01, 2021, 09:00
- 18:00
vFairs online platform (SIAM CSE21 registration required)
Contact Person

Abstract

This minisymposium brings together experts in numerical simulation that have developed HP

Thursday, October 08, 2020, 12:00
- 13:00
KAUST
We present Exascale GeoStatistics (ExaGeoStat) software, a high-performance library implemented on a wide variety of contemporary hybrid distributed-shared supercomputers whose primary target is climate and environmental prediction applications.
Thursday, July 09, 2020, 16:00
- 17:00
KAUST
Contact Person
Out-of-Core simulation systems often produce a massive amount of data that cannot fit on the aggregate fast memory of the compute nodes, and they also require to read back these data for computation. As a result, I/O data movement can be a bottleneck in large-scale simulations. Advances in memory architecture have made it feasible to integrate hierarchical storage media on large-scale systems, starting from the traditional Parallel File Systems to intermediate fast disk technologies (e.g., node-local and remote-shared NVMe and SSD-based Burst Buffers) and up to CPU’s main memory and GPU’s High Bandwidth Memory. However, while adding additional and faster storage media increases I/O bandwidth, it pressures the CPU, as it becomes responsible for managing and moving data between these layers of storage. Simulation systems are thus vulnerable to being blocked by I/O operations. The Multilayer Buffer System (MLBS) proposed in this research demonstrates a general method for overlapping I/O with computation that helps to ameliorate the strain on the processors through asynchronous access. The main idea consists in decoupling I/O operations from computational phases using dedicated hardware resources to perform expensive context switches. By continually prefetching up and down across all hardware layers of the memory/storage subsystems, MLBS transforms the original I/O-bound behavior of evaluated applications and shifts it closer to a memory-bound or compute-bound regime.
Thursday, March 05, 2020, 12:00
- 13:00
Building 9, Level 2, Room 2322
In the lecture we present a three dimensional mdoel for the simulation of signal processing in neurons. To handle problems of this complexity, new mathematical methods and software tools are required. In recent years, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.
Prof. Dmitri Kuzmin, Applied Mathematics, TU Dortmund University
Monday, February 03, 2020, 14:00
- 15:00
Building 1, Level 4, Room 4214
Contact Person
In this talk, we review some recent advances in the analysis and design of algebraic flux correction (AFC) schemes for hyperbolic problems. In contrast to most variational stabilization techniques, AFC approaches modify the standard Galerkin discretization in a way which provably guarantees the validity of discrete maximum principles for scalar conservation laws and invariant domain preservation for hyperbolic systems. The corresponding inequality constraints are enforced by adding diffusive fluxes, and bound-preserving antidiffusive corrections are performed to obtain nonlinear high-order approximations. After introducing the AFC methodology and the underlying theoretical framework in the context of continuous piecewise-linear finite element discretizations, we present some of the limiting techniques that we use in high-resolution AFC schemes. This presentation is based on joint work with Dr. Manuel Quezada de Luna (KAUST) and other collaborators.