[Vortraege] PS: talks and visitors KW5 and KW6

[Stefanie Preuss]

Dear Colleagues,

Please find below two more very interesting talks send on behalf of Prof. Norbert Mauser.

We are looking very much forward to welcome you at the MMM Seminar room!

With best regards and have a nice weekend,
Stefanie Preuss
_______________

Dear Colleagues,

2 more talks, in the context of the WPI Thematic Programs  "(Physics informed) Machine Learning and Uncertainty Quantification" and "(Quantum) Wave equations":

1)
Tuesday 31 jan  12h – 13h
MMM-WPI Seminar room (8th floor Fak. Math. OskarMorgensternPlatz 1)

Speaker: Nana Liu (INS – U. Michigan - Shanghai Jiao Tong Univ.)
www.nanaliu.weebly.com

Title: „Quantum simulation of partial differential equations via Schrödingerisation“

Abstract: In this talk, I’ll introduce a simple new way – called „Schrödingerisation“ – to simulate general linear partial differential equations via quantum simulation. Using a simple new transform, referred to as the „warped phase transformation“, any linear partial differential equation can be recast into a system of Schrödinger’s equations – in real time — in a straightforward way. This can be seen directly on the level of the dynamical equations without more sophisticated methods. This approach is not only applicable to PDEs for classical problems but also those for quantum problems – like the preparation of quantum ground states, Gibbs states and the simulation of quantum states in random media in the semiclassical limit.

2)
Tuesday 7 Feb at 14h30 – 15h30
MMM-WPI Seminar room (8th floor Fak. Math. OskarMorgensternPlatz 1)


Speaker: Shi Jin (Inst. Natural Sciences - Shanghai Jiao Tong Univ.)
http://old.ins.sjtu.edu.cn/faculty/jinshi

Title : „Consensus-based High Dimensional Global Non-convex Optimization in Machine Learning“

Abstract: We introduce a stochastic interacting particle consensus system for global optimization of high dimensional non-convex functions. This algorithm does not use gradient of the function thus is suitable for non-smooth functions. We prove, for fully discrete systems, that under dimension-independent conditions on the parameters, with suitable initial data, the algorithms converge to the neighborhood of the global minimum almost surely. We also introduce an Adaptive Moment Estimation (ADAM) based version to significantly improve its performance in high-space dimension.

Everyone welcome !

Norbert J Mauser
www.wpi.ac.at/director

Am 27.01.2023 um 11:57 schrieb Talks Mathematik:
> Sehr geehrte Fakultätsmitglieder,
>
> bitte finden Sie anbei die Vortragsankündigungen für KW 5.
>
> Die Vorträge der nächsten Tage finden Sie auch auf der Fakultätshomepage:
> https://mathematik.univie.ac.at/en/eventsnews/event-calendar/events-of-the-next-week/ 
>
>
> Wir begrüßen herzlich die Gäste der kommenden Woche:
> A. Dymak (H. Bruin)
> F. Filbir (I. Shafkulovska)
> A. Klotz (M. Ehler)
> I. Zlotnikov  (I. Shafkulovska, M. Faulhuber)
>
> Mit freunlichen Grüßen,
> Astrid Kollros-Spinka
> --------------------------------------------------------------
> Secretary Stochastics & Financial Mathematics
> Department of Mathematics
> University of Vienna
> Oskar-Morgenstern-Platz 1, Room 6.134
> A-1090 Vienna
> --------------------------------------------------------------