Title: Effects of inertia on active polymer conformation and dynamics.

Speaker: Mohammad Fazelzade (UvA)

Abstract:

Active matter consists of agents that use energy to move themselves, generate force on their surrounding environment and exhibit remarkable non-equilibrium collective phenomena. Active filaments are a subclass of active systems where interplay between internal degrees of freedom and activity generates surprising conformational and dynamical features. Many active filamentous systems such as snakes, worms and synthetic filaments have finite mass and inertia can affect their shape and dynamics. However, the effect of inertia on conformation and dynamics of active filaments remains unexplored. In this work, we investigate the inertial effects on active polymers by performing Langevin dynamics simulations in the underdamped and overdamped regimes. We find striking inertia-induced rigidity of polymer backbone. Unlike passive polymers for which inertial effects at long times vanish, the fingerprint of inertia in activepolymers is visible in enhanced mean velocity and long-time diffusion of their center of mass. We rationalize the inertia-induced enhanced dynamics by analytical calculations, applicable to any active polymer model,revealing significant contributions from time correlations of active force convoluted by inertial relaxation to velocity correlations.

Title: Tensor networks for complex systems.

Speaker: Wout Merbis (UvA)

Abstract:

Complex systems are often composed of large collections of like constituents (spins, bits, agents, etc..) with locally defined interactions,which can exhibit emergent collective behavior. They are ubiquitous in Nature and often dynamical, out of equilibrium and of high complexity. In stochastic models of complex systems, the state of the system is described by a high-dimensional probability vector (i.e. exponential in system size), which complicates any exact analytical treatment. In this talk, we will discuss the implementation of tensor network (TN) methods to obtain efficient and accuratenumerical representations of high dimensional probability vectors describingthe dynamical behavior of stochastic models. As an example, we will look at the one-dimensional contact process (known as the SIS model to epidemiologists) and use TNs to obtain distributions of gaps and study the thermodynamic limit ofthe dynamical activity in the system. We conclude with ideas for further research, which I hope to carry out at UvA in the coming two years.