Dear Colleagues,

We would like to invite you to the following (double) SPASS seminar, jointly organized by UniPi, SNS, UniFi and UniSi (abstracts below):

Matrix Whittaker processes Elia Bisi (TU Wien)

and

Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering Michele Coghi (Università di Trento)

The seminars will take place on TUE, 7.2.2023 respectively at 14:00 CET and 15:00 CET in Sala Seminari, Dipartimento di Matematica, Pisa and streamed online at the link below.

The organizers, A. Agazzi, G. Bet, A. Caraceni, F. Grotto, G. Zanco https://sites.google.com/unipi.it/spass https://www.google.com/url?q=https://sites.google.com/unipi.it/spass&source=gmail-imap&ust=1665669490000000&usg=AOvVaw07o0tKOUGmZjDDF2Ta7pQY

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Matrix Whittaker processes Abstract: Our journey starts from interacting random walks with push-and-block dynamics. We then consider their positive temperature analogues, touching upon polymer partition functions. Finally, we arrive at matrix Whittaker processes, which are integrable models of interacting Markov dynamics on matrix spaces. Our main tools are intertwining relations and the theory of Markov functions, which we will review. This talk is based on a joint work with Jonas Arista and Neil O’Connell: https://arxiv.org/abs/2203.14868.

Rough McKean-Vlasov dynamics for robust ensemble Kalman filtering Abstract: Motivated by the challenge of incorporating data into misspecified and multiscale dynamical models, we study a McKean-Vlasov equation that contains the data stream as a common driving rough path. This setting allows us to prove well-posedness as well as continuity with respect to the driver in an appropriate rough-path topology. The latter property is key in our subsequent development of a robust data assimilation methodology: We establish propagation of chaos for the associated interacting particle system, which in turn is suggestive of a numerical scheme that can be viewed as an extension of the ensemble Kalman filter to a rough-path framework. Finally, we discuss a data-driven method based on subsampling to construct suitable rough path lifts and demonstrate the robustness of our scheme in a number of numerical experiments related to parameter estimation problems in multiscale contexts.

---------------------------------------------------------------------- Gianmarco Bet (he/him) Senior researcher

https://gianmarco.bet Phone: (+39) 055 2751491

Department of Mathematics and Informatics "U. Dini" University of Florence Viale Morgagni, 65 50134 Firenze, Italy Office 64 ----------------------------------------------------------------------

---------------------------------------------------------------------- Gianmarco Bet (he/him) Senior researcher

https://gianmarco.bet Phone: (+39) 055 2751491

Department of Mathematics and Informatics "U. Dini" University of Florence Viale Morgagni, 65 50134 Firenze, Italy Office 64 ----------------------------------------------------------------------