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


News

13/12/2019



Switch2Product 2019
I progetti Amazing dicui è coordinatore scientifico la prof.ssa Simona Perotto e The FlowCatcher di cui è coordinatore scientifico il prof. Simone Vantini sono tra i vincitori dell'XI edizione di Switch2Product.

11/12/2019



ARCHAEOLOGY
Pompeii Mosaic May Depict Surveyors’ Tool

22/11/2019



Giovedì 5 dicembre 2019 - ore 18.00 - Auditorium, via Pascoli, 53

04/11/2019


29/10/2019



Festival della Scienza - Genova - 26 ottobre 2019
Cuore matematico - La scienza dei numeri per comprendere il corpo umano - Lectio Magistralis con Alfio Quarteroni

15/10/2019


15/10/2019


16/09/2019



Caccia al tesoro finanziaria

16/09/2019



Fare Educazione Finanziaria nelle scuole secondarie
Dipartimento di Matematica - 15 ottobre 2019

13/09/2019



MeetMeTonight 2019

18/07/2019



Polimifest
Il diritto di contare: introduce Alessandra Menafoglio

17/07/2019



Math and the King - ICIAM 2019
His Majesty Felipe VI of Spain officially opened the 2019 International Congress on Industrial and Applied Mathematics (ICIAM) Conference in Valencia . In this photo, King Felipe talks, among others, with professor Alfio Quarteroni, keynote speaker at the conference

27/05/2019



Aspetti linguistici dell'apprendimento della matematica nella scuola primaria
18 giugno 2019 - ore 9.30 - aula Rogers

20/05/2019



lunedì 27 maggio 2019 - ore 18.30 - aula De Donato

14/05/2019


06/05/2019


29/04/2019



Corso di Studi in Ingegneria Matematica: presentazione Laurea Magistrale
mercoledì 22 maggio 2019 - ore 17:15 - aula CI. 1

27/03/2019



Incontro tra PhD programs e Ingegneria Matematica
10 aprile 2019

26/03/2019



Faces of Geometry. From Agnesi to Mirzakhani
13 maggio 2019 Aula Magna del Politecnico di Milano

20/03/2019


05/03/2019



Premio Pianeta Galileo 2019 al Prof. Alfio Quarteroni
Premio Pianeta Galileo 2019 al Prof. Alfio Quarteroni
 more

04/03/2019



SAFARI NJEMA - From informal mobility to mobility policies through big data analysis

04/03/2019



IL POLITECNICO CURATORE DEL PADIGLIONE ITALIA ALLA TRIENNALE

28/02/2019


15/02/2019


14/02/2019



PIDAY 2019

06/02/2019


04/02/2019



Seminari di Cultura Matematica
2019 - XVIII ciclo

28/01/2019



2019 RISM Congress: Modelling the Cardiac Function - iHEART
Three days of lectures to focus on the latest achievements in modelling the heart function. For updated program and details for registration, please see http://iheart.polimi.it/mcf2019
 more

24/01/2019



GIZA, IL SOLE E LE ALTRE STELLE
Civico Planetario di Milano - 15 febbraio 2019 - ore 21.00

23/01/2019



SPORT 2.0: TRA BIG DATA E GESTIONE DEL TALENTO
Incontro con: Davide Mazzanti, allenatore Nazionale italiana di Pallavolo femminile e Piercesare Secchi, docente di Statistica, Politecnico di Milano

22/01/2019


22/01/2019


17/01/2019


15/01/2019


 Dicono di noi...

Prossimi Eventi

  • lug 16 gio 2020

    MOX Colloquia
    Gitta Kutyniok, Deep learning meets parametric partial differential equations,  16-07-2020, ore 14:00
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    • MOX Colloquia
    • Gitta Kutyniok
    • Institute of Mathematics, Technische Universität Berlin (DE)
    • Deep learning meets parametric partial differential equations
    • Giovedì 16 luglio 2020 alle ore 14:00
    • Online seminar: https://mox.polimi.it/elenco-seminari/?id_evento=1977&t=763724
    • Abstract
      High-dimensional parametric partial differential equations (PDEs) appear in various contexts including control and optimization problems, inverse problems, risk assessment, and uncertainty quantification. In most such scenarios the set of all admissible solutions associated with the parameter space is inherently low dimensional. This fact forms the foundation for the reduced basis method.
      Recently, numerical experiments demonstrated the remarkable efficiency of using deep neural networks to solve parametric problems. In this talk, after an introduction into deep learning, we will present a theoretical justification for this class of approaches. More precisely, we will derive upper bounds on the complexity of ReLU neural networks approximating the solution maps of parametric PDEs. In fact, without any knowledge of its concrete shape, we use the inherent low-dimensionality of the solution manifold to obtain approximation rates which are significantly superior to those provided by classical approximation results. We use this low-dimensionality to guarantee the existence of a reduced basis. Then, for a large variety of parametric PDEs, we construct neural networks that yield approximations of the parametric maps not suffering from a curse of dimensionality and essentially only depending on the size of the reduced basis.
      Finally, we present a comprehensive numerical study of the effect of approximation-theoretical results for neural networks on practical learning problems in the context of parametric partial differential equations. These experiments strongly support the hypothesis that approximation-theoretical effects heavily influence the practical behavior of learning problems in numerical analysis.
    • Gitta Kutyniok
      Gitta Kutyniok currently holds an Einstein Chair in the Institute of Mathematics at the Technische Universität Berlin, a courtesy appointment in the Department of Computer Science and Engineering, an Adjunct Professorship in Machine Learning at the University of Tromso, and is also the head of the Applied Functional Analysis Group. She received her Diploma in Mathematics and Computer Science as well as her Ph.D. degree from the Universität Paderborn in Germany, and her Habilitation in Mathematics in 2006 at the Justus-Liebig Universität Gießen. From 2001 to 2008 she held visiting positions at several US institutions, including Princeton University, Stanford University, Yale University, Georgia Institute of Technology, and Washington University in St. Louis. In 2008, she became a full professor of mathematics at the Universität Osnabrück, and moved to Berlin three years later.
      She received various awards for her research such as an award from the Universität Paderborn in 2003, the Research Prize of Gießen and a Heisenberg-Fellowship in 2006, the von Kaven Prize by the DFG in 2007, and an Einstein Chair in 2008. She gave the Noether Lecture at the ÖMG-DMV Congress in 2013 and the Hans Schneider ILAS Lecture at IWOTA in 2016. She also became a member of the Berlin-Brandenburg Academy of Sciences and Humanities in 2017, a SIAM Fellow in 2019, and an IEEE Senior Member in the same year.
      She was Chair of the SIAM Activity Group on Imaging Sciences from 2018-2019 and is Co-Chair of the first SIAM conference on Mathematics of Data Science taking place this year. She is also, for instance, Scientific Director of the graduate school BIMoS at TU Berlin and Chair of the GAMM Activity Groups on Mathematical Signal- and Image Processing and Computational and Mathematical Methods in Data Science, as well as of the MATH+ Activity Group on Mathematics of Data Science. Her main research interests are in the areas of applied harmonic analysis, compressed sensing, high-dimensional data analysis, imaging science, inverse problems, machine learning, numerical mathematics, partial differential equations, and applications to life sciences and telecommunication.
    • 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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DADS (Data Analytics and Decision Sciences)

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