Signals travel at finite speeds within the nerves of living organisms, between satellites and the Earth, or in computers and other technological devices. As a result, they incur delays in moving from one point to another, which engineers, roboticists, control-theorists and neuroscientists typically consider as a source of error. Read more →
Brownian motion has long been the standard paradigm for modelling random, diffusive motion, such as the haphazard movement of a dust particle floating in a fluid. This is considered to be “normal” diffusion, in which the mean square particle displacement – calculated as an average over an ensemble of particles – increases linearly in the long-time limit. Read more →
LML Fellow Mark Kirstein has received a PRIME fellowship from DAAD (the German Academic Exchange Service). This will support a 12-month research visit to the London Mathematical Laboratory, followed by a postdoctoral position at Leipzig University for 6 months. Mark will use his time at LML to study The time resolution of the probability weighting puzzle as a member of the Ergodicity Economics (EE) research group. Read more →
Economists and policy makers worry that the rapid advance of artificial intelligence (AI) and automation technologies could seriously disrupt labour markets. Read more →
Electroencephalography (EEG) provides a direct measure of neuronal activity as reflected in the scalp electrical field. Empirically, global measures of EEG topography remain stable in so-called EEG microstates for brief periods (50–100 ms) before switching to another quasi-stable state. Read more →
Here are links to a few recent articles by LML Fellow Mark Buchanan. Read more →
Engineered nanoscale structures known as artificial graphene exhibit the properties of real graphene but in a setup where it is easy to tune features such as the electronic density, lattice constant, geometry or coupling with the environment. Read more →
In the 1980s, most researchers approached empirical analysis of low-dimensional dynamical systems through the famous Takens embedding theorem, which guarantees that the attractor of any dynamical system can be reconstructed from samples of the values of key variables. Read more →
Erica’s research focusses on subtle issues affecting the use of mathematical models and simulations in aid of real-world decision making. In her own words, Read more →
Will climate change make weather forecasting harder than it currently is? Or, could a warmer planet make prediction easier? The answer isn’t obvious. Because atmospheric dynamics are chaotic, there are practical limits to predictability, regardless of data precision and available computing power. Read more →
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