Dear all,
You are all invited to this week's NOMADS seminar at GSSI.
The seminar will be given on *Wednesday December 16 at 17:00* (CET) by
*Alexander Viguerie* from GSSI.
Title, abstract and zoom link are below.
This is the last seminar of 2020 and we will then be back on Jan 13, 2021.
Further info about past and future meetings are available at the webpage:
https://num-gssi.github.io/seminar/
Please feel free to distribute this announcement as you see fit.
Hope to see you all on Wednesday!
Francesco and Nicola
==================================================
Zoom link:
https://us02web.zoom.us/j/89492628943
Speaker:
Alexander Viguerie
<https://www.gssi.it/people/post-doc/post-doc-maths/item/11289-viguerie-alex>
Title:
Efficient, stable, and reliable solvers for the Steady Incompressible
Navier-Stokes equations: application to Computational Hemodynamics.
Abstract:
Over the past several years, computational fluid dynamics (CFD)
simulations have become increasingly popular as a clinical tool for
cardiologists at the patient-specific level. The use of CFD in this area
poses several challenges. The clinical setting places heavy restrictions
on both computational time and power. Simulation results are usually
desired within minutes and are usually run on standard computers. For
these reasons, steady-state Navier-Stokes simulations are usually
preferred, as they can be completed in a fraction of the time required
to run an unsteady computation. However, in many respects the steady
problem is more difficult than the unsteady one, particularly in regards
to solving the associated linear and nonlinear systems. Additionally,
boundary data for patient-specific problems is often missing,
incomplete, or unreliable. This makes the determination of a useful
model challenging, as it requires the generation of reliable boundary
data without introducing heavy computational costs. This seminar will
address these challenges, as well as some others, and introduce new
techniques for workarounds. Results from patient-specific cases will be
presented and discussed.
—
Francesco Tudisco
Assistant Professor
School of Mathematics
GSSI Gran Sasso Science Institute
Web: https://ftudisco.gitlab.io
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Buongiorno,
vi informo che oggi alle 15:00 Desmond Higham (University of Edinburgh),
presenterà un seminario dal titolo "/A Hierarchy of //Network Models
Giving Bistability Under Triadic Closure/".
L'abstract e' allegato.
Per registrarsi e ricevere il link per partecipare via Zoom cliccare su
https://sites.google.com/unisa.it/nepaseminars
A presto,
Dario
---
Abstract: Triadic closure describes the tendency for new friendships to
form between individuals who already have friends in common. It has been
argued heuristically that the triadic closure effect can lead to
bistability in the formation of large-scale social interaction networks.
Here, depending on the initial state and the transient dynamics, the
system may evolve towards either of two long-time states. In this work,
we study a hierarchy of network evolution models that incorporate
triadic closure, building on the work of Grindrod, Higham and Parsons
[Internet Mathematics, 8, 2012, 402--423]. In a macroscale regime, we
show rigorously that a bimodal steady state distribution is admitted.
Computational simulations will be used to support the analysis. This is
joint work work with Stefano Di Giovacchino (L'Aquila) and Kostas
Zygalakis (Edinburgh).
Good morning everyone,
This is just a gentle reminder about today's seminar "From PDEs to data
science: an adventure with the graph Laplacian" by Martin Stoll
(TU-Chemnitz). Abstract below.
The seminar is at 17:00 (CET). To attend, please use the zoom link:
https://us02web.zoom.us/j/81317396646
Hope to see you there!
Francesco and Nicola
------
Martin Stoll <https://www.tu-chemnitz.de/mathematik/wire/prof.php>,
TU-Chemnitz
From PDEs to data science: an adventure with the graph Laplacian
In this talk we briefly review some basic PDE models that are used to
model phase separation in materials science. They have since become
important tools in image processing and over the last years
semi-supervised learning strategies could be implemented with these PDEs
at the core. The main ingredient is the graph Laplacian that stems from
a graph representation of the data. This matrix is large and typically
dense. We illustrate some of its crucial features and show how to
efficiently work with the graph Laplacian. In particular, we need some
of its eigenvectors and for this the Lanczos process needs to be
implemented efficiently. Here, we suggest the use of the NFFT method for
evaluating the matrix vector products without even fully constructing
the matrix. We illustrate the performance on several examples.
—
Francesco Tudisco
Assistant Professor
School of Mathematics
GSSI Gran Sasso Science Institute
Web: https://ftudisco.gitlab.io
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Speaker: Fabio Durastante
Affiliation: IAC-CNR (...that will soon become University of Pisa)
Time: Tuesday, 01/12/2020, 16:00
Title: (Sparse) Linear Algebra at the Extreme Scales
Sparse linear algebra is essential for a wide variety of scientific
applications.
The availability of highly parallel sparse solvers and preconditioners
lies at the
core of pretty much all multi-physics and multi-scale simulations.
Technology
is nowadays expanding to target exascale platforms. I am going to
present
some work on Algebraic Multigrid Preconditioners in which we try to
face these
challenges to make Exascale Computing possible.
The talk will focus on one side on the theoretical aspects pertaining
to the
construction of the multigrid hierarchy for which the main novelty is
the design
and implementation of new parallel smoothers and a coarsening algorithm
based on aggregation of unknowns employing weighted graph matching
techniques.
On the other, the talk also focuses on the libraries developed to cover
the needs of having parallel BLAS feature for sparse matrices that are
capable
of running on machines with thousands of high-performance cores; and to
discuss
the advancements made by the new smoothers and coarsening algorithm
as an improvement in terms of numerical scalability at low operator
complexity
over the algorithms available in previous releases of the package. I
will present
weak scalability results on two of the most powerful supercomputers in
Europe,
for linear systems with sizes up to O(10^10) unknowns for a benchmark
Poisson
problem, and strong scaling result for a wind-simulation benchmark
problem.
This is a joint work with P. D’Ambra, and S. Filippone. This work is
supported by the
EU under the Horizon 2020 Project Energy oriented Centre of
Excellence: toward exascale for energy (EoCoE-II), Project ID: 824158
Meeting link: <https://hausdorff.dm.unipi.it/b/leo-xik-xu4>
