Grigorios A. Pavliotis is Professor of Applied Mathematics at the Department of Mathematics at Imperial College. His main research interests lie in the areas of stochastic differential equations and diffusion processes, nonequilibrium statistical mechanics and homogenization theory for partial differential equations and stochastic differential equations. He is particularly interested in the development of analytical, computational and statistical techniques for multiscale stochastic systems, in time-dependent statistical mechanics and kinetic theory and in the analysis and development of sampling techniques in high dimensions. Current research projects include inference and control for multiscale systems, the development of computational techniques for calculating transport coefficients, homogenization for multiscale diffusion processes and sampling techniques in molecular dynamics.
His personal webpage can be found at http://www.ma.ic.ac.uk/~pavl
Gaskin T, Pavliotis GA, Girolami M, 2024, Inferring networks from time series: A neural approach, Pnas Nexus
et al., 2023, Path integrals, particular kinds, and strange things, Physics of Life Reviews, Vol:47, ISSN:1571-0645, Pages:35-62
et al., 2023, Optimal friction matrix for underdamped Langevin sampling, Esaim: Mathematical Modelling and Numerical Analysis, Vol:57, ISSN:2822-7840, Pages:3335-3371
Costa LD, Pavliotis GA, 2023, The entropy production of stationary diffusions, Journal of Physics A: Mathematical and Theoretical, Vol:56, ISSN:1751-8113, Pages:1-52
et al., 2023, Online parameter estimation for the McKean–Vlasov stochastic differential equation, Stochastic Processes and Their Applications, Vol:162, ISSN:0304-4149, Pages:481-546