Current Research

Current Research

CiT is actively researching in various mathematical fields with links to chemistry. These topics include:


Bayesian parameter estimation:

CiT is currently doing research in the field of Bayesian estimation of densities over parameter spaces. As an addition to classical parameter estimation, where one optimal parameter set is searched, the computation of a probability distribution over the set of possible parameters gives more reliable estimates of the region in which the optimal parameters lie. This is especially helpful in case the data are subject to uncontrollable, unknown or random effects. CiT uses an extended version of the well-known method Kernel Density Estimation to compute these distributions. This is already implemented in the upcoming version of Predici 11. Soon, this will include reconstruction of full molecular weights distributions from the results of Predici 11 hybrid Monte-Carlo simulations. A scientific publication is in the making.

Dynamical systems modelling:

For the mathematical prediction of the development of a complex system such as the concentration of substances in a reactor, it is vital to gain an understanding under which rules the system propagates itself over time. CiT is using and improving state-of-the-art theory and tools from the field of system identification that are used to explain even systems that show nonlinear behaviour and are subject to memory effects.

Covid-19 spreading:

CiT cooperates with a group of researchers from Freie Universität Berlin and Technische Universität Berlin to predict the evolution of COVID-19 infection numbers dependent on political measures. With the help of Predici 11, meaningful results have been created which can be found in a research article. CiT themselves conduct research on the uncertainty quantification of the projected spreading for which new Predici 11 version proves to be the perfect tool. A research article on this is planned.

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