Prof. Martin Hairer

Martin Hairer is a mathematician globally renowned for his breakthrough work at the intersection of analysis and probability. In 2014 he won the Fields Medal for visionary work which introduced a radical new way of constructing solutions for certain nonlinear stochastic partial differential equations which had been intractable before, and which are of great importance in particular to physics.

See also: Martin Hairer – Wikipedia
https://orcid.org/0000-0002-2141-6561

Main Publications

  • Friz, Peter K., and Martin Hairer. 2020. A Course on Rough Paths: With an Introduction to Regularity Structures. Second edition. Universitext. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-41556-3.
  • Hairer, Martin, and Jonathan Mattingly. 2006. “Ergodicity of the 2D Navier–Stokes Equations with Degenerate Stochastic Forcing.” Annals of Mathematics 164 (3): 993–1032. https://doi.org/10.4007/annals.2006.164.993.
  • Hairer, M., J. C. Mattingly, and M. Scheutzow. 2011. “Asymptotic Coupling and a General Form of Harris’ Theorem with Applications to Stochastic Delay Equations.” Probability Theory and Related Fields 149 (1–2): 223–59. https://doi.org/10.1007/s00440-009-0250-6.
  • Hairer, Martin, and Jonathan C. Mattingly. 2011b. “Yet Another Look at Harris’ Ergodic Theorem for Markov Chains.” In Seminar on Stochastic Analysis, Random Fields and Applications VI, edited by Robert Dalang, Marco Dozzi, and Francesco Russo, 109–17. Basel: Springer Basel. https://doi.org/10.1007/978-3-0348-0021-1_7.
  • Hairer, Martin, and Jonathan C. Mattingly. 2008. “Spectral Gaps in Wasserstein Distances and the 2D Stochastic Navier–Stokes Equations.” The Annals of Probability 36 (6). https://doi.org/10.1214/08-AOP392.
  • Hairer, Martin, and Étienne Pardoux. 2015. “A Wong-Zakai Theorem for Stochastic PDEs.” Journal of the Mathematical Society of Japan 67 (4). https://doi.org/10.2969/jmsj/06741551.
  • Hairer, Martin, and Jonathan C. Mattingly. 2011a. “A Theory of Hypoellipticity and Unique Ergodicity for Semilinear Stochastic PDEs.” Electronic Journal of Probability 16 (none). https://doi.org/10.1214/EJP.v16-875.