This workshop is organized in the frame of the G·IST - Institut de Sciences Théoriques de Genève.
More informations will come soon.
Dates: 2-6 November, 2026
Organizers: Gilles Vilmart (UNIGE), Konstantinos C. Zygalakis (University of Edinburgh)
Abstract:
As the size of data, computational resources, and models is growing at an unprecedented pace, the challenge of maintaining both performance and robustness is one of the key challenges of modern data science. In many applications, taking advantage of specific structures and geometries into large scale statistical models and complex dynamics can enhance the stability, efficiency and the reliability of numerical methods. Examples include geometric numerical integration for Hamiltonian systems, stiff integrators for multiscale dynamics, Riemannian optimization techniques.
The goal of this workshop is to bring together researchers from diverse areas of applied mathematics and statistics to focus on the foundations for high dimensional modeling and numerical analysis, to discuss the latest developments, exchange ideas, and foster new collaborations. The meeting will address recent developments in numerical analysis of complex dynamical systems, dynamical approaches for constraint optimization methods, high-dimensional problems and model reduction, stochastic dynamics and invariant measure sampling, and particularly Langevin dynamics.
Keywords: simulation of dynamical systems (37M05), discretization of structured dynamical systems (37M15 ), Diffusion processes and stochastic analysis on manifolds (58J65), Monte-Carlo methods (65C05 ), Computational methods in Markov chains (60J22), Nonconvex programming, global optimization 90C26).
Participants: Due to space limitations, participation to this workshop is by invitation only.
Venue:
Villa Boninchi
Chemin du Nant-d’Aisy 11
Corsier, Genève 1246
Switzerland
Contact: for any question please contact us at
