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Responsabile Gestionale: Dr.ssa Franca Di Censo


News

22/12/2016


08/11/2016



November 14th - November 18th 2016

21/10/2016



ENI AWARD 2016 AD ALESSANDRA MENAFOGLIO DEL POLITECNICO DI MILANO
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19/10/2016


05/10/2016


05/10/2016


22/09/2016


08/09/2016



SIMAI 2016
Politecnico di Milano - September 13, 2016 – September 16, 2016

08/09/2016



Il MOX a RIO2016
Due dei quattro atleti presenti alle Olimpiadi di Rio 2016 hanno svolto la loro tesi al MOX.
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07/07/2016



Eni Award Winners Announced
Alessandra Menafoglio ha vinto il Premio Debutto nella Ricerca, Eni Award 2016.
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22/04/2016



Seminari di Cultura Matematica
27 aprile 2016 Ore 12:15 Aula Consiglio del Dipartimento di Matematica Ed. 14 (Nave) Settimo Piano

08/04/2016



Seminari di Cultura Matematica
ore 12:15 – Aula B21, ed. 14 (Nave), Campus Bonardi, via Bonardi 9, Milano

05/04/2016



Seminari di Cultura Matematica
ore 12.15 – aula B21 - Ed.14 Campus Bonardi

15/03/2016



The 2015 SIMAI prize has been awarded to Paola Antonietti
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10/03/2016



Christian Vergara winner of the prize Gian Giacomo Drago e Fausta Rivera Drago
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08/03/2016



Aperta la strada allo sviluppo di nuovi farmaci antitumorali
Il lavoro di Pasquale Ciarletta e Chiara Giverso, del Laboratorio MOX, pubblicato su Nature Scientific Reports
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07/03/2016



Andrea Bonarini (Politecnico di Milano)
Mercoledì 16 marzo ore 11:15 – Aula Rogers, via Ampère 2, Milano

04/03/2016



Presentazione del Corso di Ingegneria Matematica

01/03/2016



Diderot Mathematical Forum 2016
Biomedical Applications of Mathematics (Paris, Madrid, Milan). March 15, 2016

18/02/2016



A cura di Renato Betti e Giulio Magli
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11/02/2016



Workshop on Semi-implicit and semi-Lagrangian methods for hyperbolic problems
MOX-Dipartimento di Matematica Politecnico di Milano, Sala Consiglio del Dipartimento di Matematica, Edificio 14 'La Nave', 1-2 Marzo 2016

11/02/2016



Fabio Peri - Più veloci della luce! Viaggi nel tempo, teletrasporto, motore a curvatura
17 febbraio 2016 ore 15.00 – Aula Consiglio, 7° piano, edificio 14 Nave – Via Bonardi, 9 – Milano

22/01/2016



Advances in Decision Making
February 3-4, 2016

07/01/2016



Marcella Lorenzi
Arte e Scienza: spazio, tempo, movimento e quarta dimensione tra futurismo e fotografia digitale

 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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