Physical ageing is a basic feature which describes the slow evolution of the mechanical, optical and electrical properties of many materials, even if left for themselves and not subjected to any apparent external forces. Ageing has been known and used for the systematic processing of materials since prehistoric times and has been studied especially in glassy materials (e.g. polymeric, monomeric, organic, metals, etc.). However its relevancy goes beyond the physics of glasses. In contrast to biological ageing, which can be conceived as a slow accumulation of defects, in physical ageing defects have the tendency to slowly disappear. Physical ageing naturally arises in many physical systems far from thermal equilibrium. A typical example is the evolution of the physical state of a magnet, quenched from a totally disordered initial state deeply into the two-phase coexistence regime. Such systems relax simultaneously towards several equivalent equilibrium states. The competition between these distinct ground states leads to (i) a very slow relaxation, (ii) the breaking of time-translation-invariance and (iii) dynamical scaling. These three defining properties of physical ageing are realised without having to fine-tune one or several thermodynamic parameters. Here, we shall illustrate how the naturally realised dynamical scaling property can be generalised towards a larger dynamical symmetry group and thereby furnishes a simple example of a dynamical local scale-invariance (LSI). The kinetics of the growth of interfaces will serve as a physical scaffold to explain this new kind of extended dynamical symmetry.
In a 2D world, most transitions towards ordered states of matter like crystals or magnets would not occur because of the increased role of fluctuations. However, non-conventional topological transitions can still occur, as understood by Kosterlitz and Thouless (2016 Nobel prize). In this talk I will present some important features of Flatland physics explored with cold atomic gases, and connect them with other prominent topological properties of matter, such as quantum-Hall type phenomena.
We first review the basic idea of using machine learning in conjunction with a limited duration of time series data to construct a closed-loop, autonomous, dynamical system that can predict the future evolution of the state of the unknown system that generated the data . Using the reservoir computing type of machine learning, we then present examples of extensions and applications of this idea. These will include a parallel implementation enabling forecasting of the states of very large spatiotemporally chaotic systems with local interactions , a hybrid scheme where an imperfect knowledge-based model component is combined with a limited-size machine learning component to achieve prediction performance much better than that of either of the components acting alone , an architecture combining the parallel and hybrid schemes, and generalization of ensemble Kalman filtering to cyclic prediction using the parallel/hybrid machine learning schemes. As an example of the potential utility of these elements for large complex spatiotemporally chaotic systems, their use in our ongoing project on improving weather forecasting  will be outlined.  Jaeger, Haas, Science (2004).  Pathak, Hunt, Girvan, Lu, Ott, Phys Rev Lett (2018).  Pathak, Wikner, Fussell, Chandra, Hunt, Girvan. Ott, CHAOS (2018).  Collaborators on our current weather forecasting project: T. Acomano, M. Girvan, B. Hunt, G. Katz, J. Reggia, I. Szunyogh, C.-Y. Wang, A. Wikner.
Fluid flows can induce long-ranged interactions and propagate information on large scales. Especially during the development of an organism, coordination on large scales is essential. What are the principal mechanisms of how fluid flows induce, transmit and respond to biological signals and thus control morphology? Fluid flows are particularly prominent during the growth and adaptation of transport networks. Here, the network-forming slime mold Physarum polycephalum emerged as a model system. Investigating the pivotal role of fluid flows in this live transport network we find that flows are patterned in a peristaltic wave across the network thereby optimizing transport. In fact, flows are hijacked by signals to propagate throughout the network. This simple mechanism is sufficient to explain surprisingly complex dynamics of the organism like scaling of peristaltic wave with network size and finding the shortest path through a maze.