Mathematical models help us increase our understanding and our predictive and control capabilities of complex environmental systems. Our team of physicists and mathematicians is engaged in the following two areas:
Modeling of hydrological and ecological systems
Environmental systems, with their many degrees of freedom and intricate interactions, seem to be too complex to be modeled, at first sight. However, many of these systems exhibit remarkably universal features that do not depend on many of the detailed interactions and are thus amenable to mathematical modeling. We apply techniques from statistical physics and nonlinear systems theory to detect and model such features in hydrological and ecological systems.
Development of algorithms
Most of our models need to be calibrated to data. Quantifying the ensuing parametric uncertainty is important if we want to use such models to make reliable predictions. Quantifying parametric uncertainty, for slow or stochastic models, is a computationally hard problem. We develop and apply both general purpose algorithms as well as tailored ones, for specific models, to tackle this problem.
