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. Soon, this will include reconstruction of full molecular weights distributions from the results of PREDICI 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.

 

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