Adam Kanigowski (University of Maryland)
Sparse Equidistribution Problems in Dynamics
10:15 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room G 43
Shuchen Guo (University of Oxford)
Abstract:
TBA
14:15 • Universität Basel, Spiegelgasse 5, Seminarraum 05.002
Dr. Nicolas Boullé (Imperial College London)
Operator learning without the adjoint abstract
Abstract:
There is a mystery at the heart of operator learning: how can one recover a non-self-adjoint operator from data without probing the adjoint? Current practical approaches suggest that one can accurately recover an operator while only using data generated by the forward action of the operator without access to the adjoint. However, naively, it seems essential to sample the action of the adjoint for learning time-dependent PDEs. In this talk, we will first explore connections with low-rank matrix recovery problems in numerical linear algebra. Then, we will show that one can approximate a family of non-self-adjoint infinite-dimensional compact operators via projection onto a Fourier basis without querying the adjoint.
16:30 • ETH Zentrum, Rämistrasse 101, Zürich, Building HG, Room G 19.2
Prof. Dr. Nicolas Curien (Equipe Probabilités et Statistique, Université Paris-Sud Orsay)
Scaling limits of the Luczak-Winkler growth algorithm abstract
Abstract:
Since the pioneering work of Aldous in the 1990s, it has been well established that large random trees converge towards a universal object: the Brownian tree. This object, which has become a pillar of modern probability, is a real random compact tree of fractal dimension 2. In this presentation, we will focus on different tree growth algorithms, such as the Rémy algorithm and the Luczak-Winkler algorithm. We will see how, by passing them to the limit, they give rise to diffusions taking values in the space of real trees, of which the Brownian tree constitutes the invariant law.
17:15 • UZH Irchel, Winterthurerstrasse 190, Zürich, Building Y27, Room H12