General resource: useful articles on the SARS-CoV-2 coronavirus pandemic (last updated 24 March 2020)

This blog post lists a number of articles and scientific papers which may be of interest in following the unfolding coronavirus epidemic. It has been assembled from suggestions made by LML Fellows and associates. Read more

Escape from model-land

Policy makers in areas ranging from public health to weather forecasting or economics rely on mathematical models to inform their decisions. As models grow more complex and powerful, one might expect, they should contribute to better decisions. Read more

Machines learn from biology, the speed of coronavirus and how to build an ethical self-driving ca

Here are links to a few recent articles by LML External Fellow Mark Buchanan. Read more

Bifurcations on Fully Inhomogeneous Networks

One of the most powerful methods of bifurcation theory is centre manifold reduction, in which a judicious coordinate change greatly simplifies the analysis of dynamical systems in the vicinity of a bifurcation point. Read more

Superextreme Waves Generation in the Linear Regime

So-called extreme or rogue waves are large amplitude waves which appear unpredictably in optical and acoustic systems, in plasmas, as well as in quantum physics and in hydrodynamics. Read more

Should We Be Afraid of Artificial Intelligence?

Economists worry about the impact that artificial intelligence (AI) technology could have as it begins to displace human employees, especially those in entry-level jobs such as data entry, customer service or retail. Read more

Diagnosing concurrent drivers of weather extremes: application to warm and cold days in North America

Extreme weather events emerge out of the interaction of many physical processes, and understanding how is a key challenge in atmospheric science. Read more

Sampling hyperspheres via extreme value theory: implications for measuring attractor dimensions

Advancing computational power has encouraged the analysis of large, high-dimensional data sets with machine learning and data mining techniques, as well as the use of algorithms to compute dynamical indicators such as Lyapunov exponents or generalized dimensions. Read more

Forecasting the magnitude of the largest expected earthquake

Geophysicists still lack a comprehensive understanding of the mechanisms and stochastic dynamics behind the earthquake generation process, and so also lack an ability to make reliable predictions of the likelihood of extreme events. Read more

Epidemic spreading with awareness and different timescales in multiplex networks

Efforts to control epidemics rely on mathematical and computational models of how infectious agents spread. Such models help to find ways to deter transmission – through vaccination and quarantine, for example, or information campaigns to alter human behaviour. Read more