Page 68 - 8th European Congress of Mathematics ∙ 20-26 June 2021 ∙ Portorož, Slovenia ∙ Book of Abstracts
P. 68
HIRZEBRUCH LECTURE
A mathematical journey through scales
Martin Hairer, m.hairer@imperial.ac.uk
Imperial College London, United Kingdom
The tiny world of particles and atoms and the gigantic world of the entire universe are separated
by scales spanning about forty orders of magnitudes. As we move from one to the other, the
laws of nature can behave in drastically different ways, going from quantum physics to general
relativity through Newton’s classical mechanics, not to mention other intermediate "ad hoc"
theories. Understanding the way in which the behaviour of mathematical models changes as we
move from one scale to another is one of the great classical questions in mathematics and theo-
retical physics. The aim of this talk is to explore how these questions still inform and motivate
interesting problems in probability theory and why so-called toy models, despite their super-
ficially playful character, can sometimes lead to useful quantitative (and not just qualitative)
predictions.
66
A mathematical journey through scales
Martin Hairer, m.hairer@imperial.ac.uk
Imperial College London, United Kingdom
The tiny world of particles and atoms and the gigantic world of the entire universe are separated
by scales spanning about forty orders of magnitudes. As we move from one to the other, the
laws of nature can behave in drastically different ways, going from quantum physics to general
relativity through Newton’s classical mechanics, not to mention other intermediate "ad hoc"
theories. Understanding the way in which the behaviour of mathematical models changes as we
move from one scale to another is one of the great classical questions in mathematics and theo-
retical physics. The aim of this talk is to explore how these questions still inform and motivate
interesting problems in probability theory and why so-called toy models, despite their super-
ficially playful character, can sometimes lead to useful quantitative (and not just qualitative)
predictions.
66
