Marija Vucelja

Marija Vucelja's research field is nonequilibrium statistical physics, with applications to soft-condensed matter and computational physics (physics of sampling). 

Lately, Marija has focused on anomalous thermal relaxations of physical systems, systems far from equilibrium, and efficient randomized algorithms (such as those utilizing non-reversible Markov Chains) designed to evaluate marginals and inference.

Over the years, Marija studied the mixing and clustering of particles in flows, problems relevant to understanding the formation of rain droplets and planetesimals, clumping of pollutants on water surfaces, and industrial applications. She derived the compressibility of surface flows and described the aggregation-disorder transition of particles in flows. Using “chaotic mixing,” she substantially accelerated specific Monte Carlo algorithms (the main numerical tools for studying complex systems). Next, Marija investigated the emergence of clones in populations. Drawing analogies between glassy systems and population dynamics, she calculated the coalescence rate (the probability of two individuals belonging to the same clone).

• M. R. Walker and M. Vucelja, Mpemba effect in terms of mean first passage time, arXiv: 2212.07496 (2022)
• M. R. Walker and M. Vucelja, Anomalous thermal relaxation of Langevin particles in a piecewise-constant potential, J. Stat. Mech. 113105 (2021)
• I. Klich, O. Raz, O. Hirschberg, and M. Vucelja, The Mpemba index and anomalous relaxation, Phys. Rev. X., 9, 2, 021060 (2019)
• M. Vucelja, K. S. Turitsyn, and M. Chertkov, Extreme-value statistics of work done in stretching a polymer in a gradient flow,  Phys. Rev. E 91, 022123 (2015)
• K. S. Turitsyn, M. Chertkov, and M. Vucelja, Irreversible Monte Carlo Algorithms for Efficient Sampling, Physica D 240, 410 - 414 (2011)