NumPI January 2021

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6 discussions

Fwd: Summer School plus Conference on “Mathematics for Nonstationary Signals,and applications in Geophysics and other fields” - L'Aquila (Italy) and online, July 2021
by Federico Poloni 30 Jan '21

30 Jan '21

Inoltro questo annuncio per i potenziali interessati non iscritti al GNCS. Buon week-end, -federico -------- Forwarded Message -------- Subject: Summer School plus Conference on “Mathematics for Nonstationary Signals,and applications in Geophysics and other fields” - L'Aquila (Italy) and online, July 2021 Date: Sat, 30 Jan 2021 10:29:15 +0100 From: Ruggiero Valeria <valeria.ruggiero(a)unife.it> To: gncs-aderenti(a)altamatematica.it Dear Colleagues, we kindly inform you that a Summer School plus Conference on “Mathematics for Nonstationary Signals and applications in Geophysics and other fields”, will take place at the Università degli Studi dell'Aquila, L'Aquila, Italy, and online on July 19-24, 2021. The event will be hybrid, providing the opportunity to everyone to join either in-person or virtually. During the Summer School young researchers and PhD students will have a chance to learn and deepen their knowledge on Mathematics of Signal Processing, in particular on new data analysis tools/techniques for non-stationary time series and their theoretical foundation. The summer school will take place during the first 4 days and it will consist of three courses of 8 hours each. Confirmed Lecturers: Patrick Flandrin - ENS Lyon Yang Wang - HKSTU Hau-tieng Wu - Duke University At the end of the school there will be a 2 days and half Conference and Poster Session during which the speakers will show both the applications of these techniques to real life data and present the current frontiers of the theoretical research. Some slots for contributed talks and posters are still available. Contributed talks will be 30 minutes long (25+5 for questions). Submission deadline is April 30, 2021. Applications for prospective students of the Summer School, as well as speakers of the conference and poster session are now open. Financial support is available for a limited number of participants. For more information and to apply please visit www.cicone.com/NoSAG21.html <http://www.cicone.com/NoSAG21.html> Best regards, The local organizing committee Antonio Cicone - DISIM - Università degli Studi dell'Aquila - L'Aquila Giulia D'Angelo - INAF - Istituto di Astrofisica e Planetologia Spaziali - Roma Enza Pellegrino - DIIIE - Università degli Studi dell'Aquila - L'Aquila Mirko Piersanti - INFN - Universita di Roma "Tor Vergata" - Roma Angela Stallone - INGV - Istituto Nazionale di Geofisica e Vulcanologia - Roma

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Math Colloquium - Anders Hansen - Jan 28
by Francesco Tudisco 28 Jan '21

28 Jan '21

Dear all, * *the next GSSI Math Colloquium will be held on *Thursday January 28 *at***3pm* (Italian time). The speaker is Anders Hansen, <http://www.damtp.cam.ac.uk/research/afha/anders/> with a lecture connecting computational mathematics with deep learning and AI. More details below. Anders Hansen is Associate Professor at University of Cambridge, where he leads the Applied Functional and Harmonic Analysis group, and Full Professor of Mathematics at the University of Oslo. To attend the talk please use to the following *Zoom link*: https://us02web.zoom.us/j/84038062394 Please feel free to distribute this announcement as you see fit. Looking forward to seeing you all on Thursday! Paolo Antonelli, Stefano Marchesani, Francesco Tudisco and Francesco Viola --------------------- Title: On the foundations of computational mathematics, Smale's 18th problem and the potential limits of AI Abstract: There is a profound optimism on the impact of deep learning (DL) and AI in the sciences with Geoffrey Hinton concluding that 'They should stop training radiologists now'. However, DL has an Achilles heel: it is universally unstable so that small changes in the initial data can lead to large errors in the final result. This has been documented in a wide variety of applications. Paradoxically, the existence of stable neural networks for these applications is guaranteed by the celebrated Universal Approximation Theorem, however, the stable neural networks are never computed by the current training approaches. We will address this problem and the potential limitations of AI from a foundations point of view. Indeed, the current situation in AI is comparable to the situation in mathematics in the early 20th century, when David Hilbert’s optimism (typically reflected in his 10th problem) suggested no limitations to what mathematics could prove and no restrictions on what computers could compute. Hilbert’s optimism was turned upside down by Goedel and Turing, who established limitations on what mathematics can prove and which problems computers can solve (however, without limiting the impact of mathematics and computer science). We predict a similar outcome for modern AI and DL, where the limitations of AI (the main topic of Smale’s 18th problem) will be established through the foundations of computational mathematics. We sketch the beginning of such a program by demonstrating how there exist neural networks approximating classical mappings in scientific computing, however, no algorithm (even randomised) can compute such a network to even 1-digit accuracy (with probability better than 1/2). We will also show how instability is inherit in the methodology of DL demonstrating that there is no easy remedy, given the current methodology. Finally, we will demonstrate basic examples in inverse problems where there exists (untrained) neural networks that can easily compute a solution to the problem, however, the current DL techniques will need 10^80 data points in the training set to get even 1% success rate. — Francesco Tudisco Assistant Professor School of Mathematics GSSI Gran Sasso Science Institute Web: https://ftudisco.gitlab.io -- You received this message because you are subscribed to the Google Groups "nomads-list" group. To unsubscribe from this group and stop receiving emails from it, send an email to nomads-list+unsubscribe(a)gssi.it. To view this discussion on the web visit https://groups.google.com/a/gssi.it/d/msgid/nomads-list/b3099d6a-bcef-2052-…. For more options, visit https://groups.google.com/a/gssi.it/d/optout.

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Postdoc Position, Krylov Methods, Charles Univ, Czech Rep
by Federico Poloni 25 Jan '21

25 Jan '21

Buongiorno, inoltro questo annuncio, che può essere di interesse per qualche iscritto alla lista. **Postdoc Position, Krylov Methods, Charles Univ, Czech Rep** A postdoc position is available within the framework of the Primus Research Programme "A Lanczos-like Method for the Time-Ordered Exponential" at the Faculty of Mathematics and Physics, Charles University, Prague. The appointment period is one year, with the possibility of extension. The postdoc will start before the end of 2021. The start date is negotiable. We are looking for candidates with a strong background in numerical linear algebra. In particular, we seek applicants with expertise in matrix function approximation and Krylov subspace methods. The applicant must hold a Ph.D. degree by the start date. Application deadline: March 15, 2021. More information and application instructions: https://www.starlanczos.cz/open-positions <https://www.starlanczos.cz/open-positions> -- --federico poloni Dipartimento di Informatica, Università di Pisa https://www.di.unipi.it/~fpoloni/ tel:+39-050-2213143

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NEPA Seminar (D. Bini)
by Leonardo Robol 15 Jan '21

15 Jan '21

Dear all, on January 19, 3 pm, Dario Bini will give a talk on "Solving Structured Matrix Equations Encountered in the Analysis of Stochastic Processes". The talk is part of the NEPA seminar series, and many more talks will take place in the next weeks. Participation is free, but a registration is required to obtain the Zoom link [1]. [1] https://sites.google.com/unisa.it/nepaseminars Best wishes, -- Leonardo.

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[NOMADS] Seminar - Gianluca Ceruti - Today at 17:00
by Francesco Tudisco 13 Jan '21

13 Jan '21

Good morning everyone, This is just a gentle reminder about today's seminar "Numerical integrators for dynamical low-rank approximation" by Gianluca Ceruti (Uni Tuebingen). Abstract below. The seminar is at 17:00 (CET). To attend please use the zoom link: https://us02web.zoom.us/j/82131676880 Hope to see you there! Francesco and Nicola ----------- Gianluca Ceruti <https://na.uni-tuebingen.de/~ceruti/> - University of Tuebingen Numerical integrators for dynamical low-rank approximation Discretization of time-dependent high-dimensional PDEs suffers of an undesired effect, known as curse of dimensionality. The amount of data to be stored and treated, grows exponentially, and exceeds standard capacity of common computational devices. In this setting, time dependent model order reductions techniques are desirable. In the present seminar, together with efficient numerical integrators, we present a recently developed approach: dynamical low-rank approximation. Dynamical low-rank approximation for matrices will be firstly presented, and a numerical integrator with two remarkable properties will be introduced: the matrix projector splitting integrator. Based upon this numerical integrator, we will construct two equivalent extensions for tensors, multi-dimensional arrays, in Tucker format - a high-order generalization of the SVD decomposition for matrices. These extensions are proven to preserve the excellent qualities of the matrix integrator. To conclude, via a novel compact formulation of the Tucker integrator, we will further extend the matrix and Tucker projector splitting integrators to the most general class of Tree Tensor Networks. Important examples belonging to this class and of interest for applications are given, but not only restricted to, by Tensor Trains. This seminar is based upon a joint work with Ch. Lubich and H. Walach. — Francesco Tudisco Assistant Professor School of Mathematics GSSI Gran Sasso Science Institute Web: https://ftudisco.gitlab.io -- You received this message because you are subscribed to the Google Groups "nomads-list" group. To unsubscribe from this group and stop receiving emails from it, send an email to nomads-list+unsubscribe(a)gssi.it. To view this discussion on the web visit https://groups.google.com/a/gssi.it/d/msgid/nomads-list/46a4a726-fee9-9f0b-…. For more options, visit https://groups.google.com/a/gssi.it/d/optout.

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[NOMADS] Seminar - Gianluca Ceruti - Jan 13 at 17:00
by Francesco Tudisco 11 Jan '21

11 Jan '21

Dear all, We hope you all had joyful holidays and wish you all a great start for the new year! We are ready to start with this year's NOMADS seminar at GSSI and would like to invite you to this week's talk. The seminar will be given on Wednesday January 13 at 17:00 (CET) by Gianluca Ceruti from University of Tuebingen. Title, abstract and zoom link are below. Further info about past and future meetings are available at the webpage: https://num-gssi.github.io/seminar/ Please feel free to distribute this announcement as you see fit. Hope to see you all on Wednesday! Francesco and Nicola ================================ Speaker: Gianluca Ceruti, University of Tuebingen https://na.uni-tuebingen.de/~ceruti/ Zoom link: https://us02web.zoom.us/j/82131676880 Numerical integrators for dynamical low-rank approximation Discretization of time-dependent high-dimensional PDEs suffers of an undesired effect, known as curse of dimensionality. The amount of data to be stored and treated, grows exponentially, and exceeds standard capacity of common computational devices. In this setting, time dependent model order reductions techniques are desirable. In the present seminar, together with efficient numerical integrators, we present a recently developed approach: dynamical low-rank approximation. Dynamical low-rank approximation for matrices will be firstly presented, and a numerical integrator with two remarkable properties will be introduced: the matrix projector splitting integrator. Based upon this numerical integrator, we will construct two equivalent extensions for tensors, multi-dimensional arrays, in Tucker format - a high-order generalization of the SVD decomposition for matrices. These extensions are proven to preserve the excellent qualities of the matrix integrator. To conclude, via a novel compact formulation of the Tucker integrator, we will further extend the matrix and Tucker projector splitting integrators to the most general class of Tree Tensor Networks. Important examples belonging to this class and of interest for applications are given, but not only restricted to, by Tensor Trains. This seminar is based upon a joint work with Ch. Lubich and H. Walach. — Francesco Tudisco Assistant Professor School of Mathematics GSSI Gran Sasso Science Institute Web: https://ftudisco.gitlab.io -- You received this message because you are subscribed to the Google Groups "nomads-list" group. To unsubscribe from this group and stop receiving emails from it, send an email to nomads-list+unsubscribe(a)gssi.it. To view this discussion on the web visit https://groups.google.com/a/gssi.it/d/msgid/nomads-list/CAO2_FW%3DqD1z%3D9V…. For more options, visit https://groups.google.com/a/gssi.it/d/optout.

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NumPI January 2021