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Direttore Vicario: Prof. Gabriele Grillo
Responsabile Gestionale: Dr.ssa Franca Di Censo


News

19/12/2017



SCIENZA in SCENA ATTO 1!

15/12/2017



Presentati i progetti Polisocial Award - il Progetto TEEN vincitore della competizione
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14/12/2017



quando la matematica prende forma.......

04/12/2017



Premio INDAM, SIMAI e UMI

15/11/2017


13/11/2017



Colazione in laboratorio: il Rettore incontra il QFinLab

05/10/2017



4 ottobre 2017: la prima CACCIA AL TESORO FINANZIARA

28/09/2017


13/09/2017



Meet Me Tonight 2017
29 & 30 settembre Giardini Indro Montanelli

26/07/2017



Colazione al Lab Mox: il rettore incontra i giovani ricercatori
25 luglio 2017

17/05/2017



PhD and PostDoc Positions at MOX - ERC Project iHEART
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15/05/2017



MOOC “Finanza per Tutti”

15/05/2017



Il Corso di Studi in Architettura incontra Massimo Osanna
giovedì 18 maggio alle ore 14.15 in aula 9.02
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11/05/2017



Euler Lecture 2017 tenuta da A. Quarteroni
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02/05/2017



Sentire la Matematica
Un Week End di Matematica e Arte

30/03/2017



ERC Advanced Grant ad Alfio Quarteroni
 more

17/03/2017



presso lo stand di Ingegneria Matematica (posizionato nel chiostro dell’edificio 3)

15/03/2017



Al Dr. Mikel Landajuela, postdoc al MOX, è stato assegnato il SMAI-GAMNI prize 2017 per la migliore tesi di dottorato francese dell'anno in metodi numerici per scienze meccaniche e di ingegneria

09/02/2017



Polimi Finance Lunch Seminar 2017

06/02/2017



Seminari di Cultura Matematica anno 2017 XVI ciclo
a cura di Anna Maria Paganoni e Giulio Magli

10/01/2017



Incontro con Syusy Blady
25 gennaio 2017 - ore 15.00 Aula Rogers - Via Ampere 2

10/01/2017


 Dicono di noi...

Prossimi Eventi

  • ott 29 gio 2020

    MOX Colloquia
    Jan S. Hesthaven, Nonintrusive reduced order models using physics informed neural networks,  29-10-2020, ore 14:00 precise
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    • MOX Colloquia
    • Jan S. Hesthaven
    • Chair of Computational Mathematics and Simulation Science, EPFL, Lausanne, CH
    • Nonintrusive reduced order models using physics informed neural networks
    • Giovedì 29 ottobre 2020 alle ore 14:00 precise
    • Online seminar: https://mox.polimi.it/elenco-seminari/?id_evento=1979&t=763724
    • Abstract
      The development of reduced order models for complex applications, offering the promise for rapid and accurate evaluation of the output of complex models under parameterized variation, remains a very active research area. Applications are found in problems which require many evaluations, sampled over a potentially large parameter space, such as in optimization, control, uncertainty quantification, and in applications where a near real-time response is needed. However, many challenges remain unresolved to secure the flexibility, robustness, and efficiency needed for general large-scale applications, in particular for nonlinear and/or time-dependent problems.

      After giving a brief general introduction to projection based reduced order models, we discuss the use of artificial feedforward neural networks to enable the development of fast and accurate nonintrusive models for complex problems. We demonstrate that this approach offers substantial flexibility and robustness for general nonlinear problems and enables the development of fast reduced order models for complex applications.

      In the second part of the talk, we discuss how to use residual based neural networks in which knowledge of the governing equations is built into the network and show that this has advantages both for training and for the overall accuracy of the model.

      Time permitting, we finally discuss the use of reduced order models in the context of prediction, i.e. to estimate solutions in regions of the parameter beyond that of the initial training. With an emphasis on the Mori-Zwansig formulation for time-dependent problems, we discuss how to accurately account for the effect of the unresolved and truncated scales on the long term dynamics and show that accounting for these through a memory term significantly improves the predictive accuracy of the reduced order model.

      Contatto: alfio.quarteroni@polimi.it
    • Jan S. Hesthaven
      After receiving his PhD in 1995 from the Technical University of Denmark, Professor Hesthaven joined Brown University, USA where he became Professor of Applied Mathematics in 2005. In 2013 he joined EPFL as Chair of Computational Mathematics and Simulation Science and since 2017 as Dean of the School of Basic Sciences. His research interests focus on the development, analysis, and application of high-order accurate methods for the solution of complex time-dependent problems, often requiring high-performance computing. A particular focus of his research has been on the development of computational methods for problems of linear and non-linear wave problems and the development of reduced basis methods, recently with an emphasis on combining traditional methods with machine learning and neural networks with broad applications, including structural health monitoring.

      He has received several awards for both his research and his teaching, and has published 4 monographs and more than 160 research papers. He is on the editorial board of 8 journals and serves as Editor-in-Chief of SIAM J. Scientific Computing.

      Homepage - https://www.epfl.ch/labs/mcss/
    • Politecnico di Milano, Dipartimento di Matematica edificio 14, via Giuseppe Ponzio 31/P, 20133 Milano - Telefono: +39 02 2399 4505 - Fax: +39 02 2399 4568

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