This seminar series gives visitors and staff members the opportunity to explain the context and aims of their work. During semester, the SMRI seminar is usually on Thursdays from 1 pm — 2 pm, followed by afternoon tea. These research talks cover any field in the mathematical sciences, and should be presented in a way that is understandable and interesting to a broad audience. Seminar information and recordings can be found below and in the SMRI Seminar YouTube playlist.
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Upcoming:
SMRI Seminar,
Hans Boden, McMaster University
Thursday 5th March 2026 at 1 pm, SMRI Seminar Room (A12 Macleay Room 301)
Abstract: TBA
Past:
SMRI Seminar, ‘Learning Theory for Neural Operators’
Jakob Zech, Heidelberg University
Thursday 26th February, 2026
Abstract: In this talk, we present results on the approximability and data requirements necessary to learn surrogates of nonlinear mappings between infinite-dimensional spaces. Such surrogate models have a wide range of applications and can be used in uncertainty quantification and parameter estimation problems in fields such as classical mechanics, fluid mechanics, electrodynamics, and earth sciences. Our analysis shows that, for certain neural network architectures, empirical risk minimization based on noisy input-output pairs can overcome the curse of dimensionality. Additionally, we provide a numerical comparison to other approaches including classical constructive methods.
SMRI Seminar, ‘Cohomology of p-adic period domains’
David Hansen, National University of Singapore
Thursday 22nd January, 2026
Abstract: Since the pioneering work of Drinfeld, p-adic period domains have been a driving force behind many developments in nonarchimedean geometry. However, despite many advances, the basic question of computing their cohomology has remained completely open outside of a few very special cases treated by Drinfeld and Schneider-Stuhler over 30 years ago. In this talk I will present a general formula for the cohomology of these spaces. This formula is elementary, and it has several unusual features which suggest some very rich phenomenology. I will explain this formula and where it comes from, present several examples, and take the first steps towards unraveling the patterns this formula is hiding.