Statistical Physics of Complex Systems

Research Topics

Biophysics and soft matter physics: biological and physical active matter, biophysics of the cell, protein diffusion, biological motors, ionic transport, nanotubes and nanopores, polymers and DNA physics, membranes, and vesicles.
Social physics: game theory and competition, collective motion and collective decision-making processes in animal and human groups, social psychology, robotics, virtual reality, machine learning.

Physics of long-range interacting systems: self-gravitating gases, two-dimensional turbulence and stratified fluids, kinetic theories, non-Boltzmann quasi-equilibrium states, applications in astrophysics and cosmology.
Probability theory and stochastic processes and their applications: reaction-diffusion models, dynamic interfaces, persistence, chemotaxis, diffusion in the presence of disorder or absorbers, optimization problems.
Strongly out-of-equilibrium systems: dynamic phase transitions, phase separation, coarsening, and glassy dynamics.

In addition to numerous collaborations with experimental groups, several members of the PhyStat team are involved in the design and even the execution of experiments. Alongside analyzing experimental data, these physicists develop methods to extract parameters or signatures of phenomena linked to theoretical models from experimental measurements.
Stochastic processes and stochastic equations of motion are common tools used by all members of PhyStat, whether applied to mathematical models studied analytically or simulated numerically.

In various contexts, the team exploits a wide range of mathematical tools from statistical physics for systems composed of large numbers of interacting particles: field theory, kinetic theories, Bethe ansatz…

From a numerical perspective, team members use various methods derived from statistical physics, such as Monte Carlo simulations, molecular dynamics, fluid equations coupled with matter, as well as diverse optimization techniques and machine learning.