[Dottorcomp] Inaugural lecture PAV-IA

[Siam Student Chapter]

Dear all,

We're pleased to invite you to the inaugural lecture of the PAV-IA seminars
which will be held at the 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 organization purposes we kindly request to *complete the following
google form <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, 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, 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:        locandina_pav_IA.pdf
Tipo:        application/pdf
Dimensione:  1909222 bytes
Descrizione: non disponibile
Url:         http://ipv01.unipv.it/pipermail/dottorcomp/attachments/20241220/29015db6/attachment-0001.pdf