Data Assimilation is concerned with the reconstruction of the state of a dynamical system from measured data. It is usually employed in a cycled way, where data are used to correct or guide the dynamical system step by step, while forecasts are generated on a desired time grid. This is a key ingredient for important applications, for example for Numerical Weather Prediction (NWP) or predictions of Climate Change, but it is also increasingly employed in medical and industrial applications.The…See More

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.

This group focuses on modeling the activity of neural masses, rather than of individual neurons. Of particular interest are "mean field" type of methods: their mathematical analysis, numerical computation, and "real world" application.