[Dottorcomp] Reminder: Mandatory registration for the PAV-IA inaugural lecture

[Siam Student Chapter]

Dear all,

This is a gentile reminder about the inaugural lecture of the PAV-IA, which
will be held in Aula Beltrami in the Department of Mathematics, University
of Pavia on *January** 14th 2025 from 14:30 to 15:30 followed by a coffee
break*!

*For organizational purposes, we remind you that registration is free but
mandatory. You can find the form at the following link.
<https://forms.gle/ZT2fTaMQo12JBc8w7>*


*Lecture Details:*

*    Speaker:  *

Francesco Regazzoni <https://regazzoni.faculty.polimi.it/> Politecnico di
Milano

*    Title:  *

Integrating physics-based models with machine learning for fast and
accurate simulations

*    Abstract: *

Mathematical models based on differential equations, such as Partial
Differential Equations (PDEs) and Stochastic Differential Equations (SDEs),
can yield quantitative predictions of physical processes. However, model
development requires a deep understanding of the physical processes, that
is not always available. Furthermore, the computational cost that
accompanies the (possibly many-query) numerical approximation of such
mathematical models may be prohibitive and hinder their use in relevant
applications. In this talk, we present scientific machine learning methods
that integrate physical knowledge with data-driven techniques to accelerate
the evaluation of differential models and address many-query problems -
such as sensitivity analysis, robust parameter estimation, and uncertainty
quantification. To speed up input-output evaluations, we present Universal
Solution Manifold Networks <https://arxiv.org/pdf/2204.07805>, namely
emulators of differential models capable of predicting spatial outputs and
accounting for the variability of the computational domain. Our method is
based on a mesh-less architecture, thus overcoming the limitations
associated with image segmentation and mesh generation required by
traditional discretization methods, and encodes geometrical variability
through an automatic shape encoding technique. Furthermore, we present Latent
Dynamics Network <https://www.nature.com/articles/s41467-024-45323-x>, a
space-time operator-learning method, which shows, tested in challenging
problems, superior accuracy (normalized error 5 times smaller) with
significantly fewer trainable parameters (more than 10 times fewer) than
state-of-the-art methods. Numerical results demonstrate that these
scientific machine learning methods enhance efficiency and accuracy in
approximating quantities of interest, as well as in solving parameter
estimation and uncertainty quantification problems.

*    Where and when: *

Tuesday, January 14th, from 14:30 to 15:30 , at the Aula Beltrami,
Department of Mathematics, University of Pavia.



We look forward to your participation!

Stay updated on SIAM Chapter activities by visiting our website
<https://sites.google.com/view/siam-unipv/home/>.

Sincerely,

PAV-IA Organizing Committee
-------------- parte successiva --------------
Un allegato non testuale è stato rimosso....
Nome:        poster.pdf
Tipo:        application/pdf
Dimensione:  2019633 bytes
Descrizione: non disponibile
Url:         http://ipv01.unipv.it/pipermail/dottorcomp/attachments/20250107/c02ae9ca/attachment-0001.pdf