Phase transition, scaling of moments, and order-parameter distributions in Brownian particles and branching processes with finite-size effects

Random walks provide precise mathematical models for diffusion processes, while rooted trees offer a geometric representation of branching processes. Both are of broad importance in probability theory and statistical physics, and some important mathematical results establish links between the two. Read more

Predicting chaos with an optimal combination of data and prior knowledge

Early in the 20th century, experts tried to forecast the weather by noting current conditions, patterns of winds, temperatures and air pressures, and looking into historical records to find previous moments when similar conditions prevailed. Looking a few days forward in the records, they could then make predictions by assuming the atmosphere would evolve as it had in the past. Read more

Synchronisation and Extreme Value Theory for Coupled Map Lattices

Coupled Map Lattices (CML) are discrete time and space dynamical systems often used as simplified models for the study of spatially-extended non-linear systems. Read more

Weak Galilean invariance as a selection principle for coarse-grained diffusive models

Galileo Galilei famously stated the principle of Galilean invariance, which links the equations of motion of closed systems as viewed in distinct inertial frames translating relative to one another at a constant velocity. Read more

Universal behavior of the full particle statistics of one-dimensional Coulomb gases with an arbitrary external potential

Random matrix theory is central to the study of the properties of strongly correlated systems in condensed matter physics and related fields, where important physical quantities are surprisingly well reflected by the joint probability density of the eigenvalues of random matrices. Read more

Intermittent dynamics in complex systems driven to depletion

From earthquakes driven by continental drift to businesses altering their strategies in response to customers’ behaviour, many complex systems exhibit highly unpredictable dynamics, fluctuating episodically between periods of relative quiescence and bursts of activity. Read more

Using Extreme Value Theory to Characterize Chaotic Dynamics

In the analysis of dynamical systems, the correlation dimension is a useful indicator describing the fractal structure of invariant sets. Other measures, such as the Lyapunov exponents and the entropy, provide complementary information on the time scale of predictability of the system. Read more

Characterizing local energy transfers in atmospheric flows

Computational models employed for simulations of weather and climate have limited spatial resolution, currently around 2 km for regional weather models and 100 km for global climate models. Read more

Large deviation theory for diluted Wishart random matrices

Modern computing technology has brought about the era of Big Data, and a host of new challenges concerning how to analyse high-dimensional data for problems ranging from studies of climate to genetics, biomedical imaging to economics. Read more

Switching dynamics in atmospheric flow

The jet stream is a band of air that flows around the Earth at high-altitude, moving West to East in mid-latitude regions. This so-called “zonal” flow typically has cyclones and anticyclones embedded within it, which last for a few days. Read more