The study of random matrices finds many important applications in physics, mathematics, biology, statistics and finance, as random matrix ensembles offer simple but nontrivial models of strongly correlated systems. Read more
Humans make decisions in part by drawing on their previous related experiences. But human memory isn’t exhaustive and complete. Read more
Many distinct physical factors help trigger earthquakes, among them the loading of the earth’s crust by water. Many studies have demonstrated a seasonal modulation of seismicity linked to water forcing in volcanic areas, plate boundary zones and other regions. Read more
The emergence of cities is a relatively recent phenomenon in human history. Their appearance in the archaeological record coincides with the advent of increasingly sedentary, agricultural, bureaucratic, and politically asymmetrical societies. Read more
Probabilistic earthquake forecasts estimate the spatial and/or temporal evolution of seismicity and offer practical guidance to authorities during earthquake sequences, especially following notable earthquakes. Read more
Complex networks can be analysed using a variety of statistical models including classic Erdös–Rényi random graphs, latent space models, configuration graphs and others. These models generally specify some structure – arising from the existence of communities, for example – but the order in which the edges are added is of no importance. Read more
Tropical cyclones are among the most extreme and costly weather events. In the United States, hurricane Katrina in 2005 alone caused damage equal to roughly 1% of the country’s gross domestic product. Read more
Large earthquakes generate aftershocks, the spatiotemporal patterns of which reveal details of how the strain energy in the Earth’s crust relaxes following a main rupture. Prior research has established that aftershocks correlate with regions of low co-seismic slip – slip which occurs at the instant of an earthquake – and of increased shear stress. Read more
Here are links to a few recent articles by LML External Fellow Mark Buchanan. Read more
In foraging for food, biological organisms explore terrain in search of irregularly dispersed sources. Mathematically, such foraging processes can be modelled using stochastic processes, and in the simplest paradigm – Gaussian spreading generated by random walks or Brownian motion – the mean square displacement of an ensemble of moving agents grows linearly in time. Read more
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