Building mathematical models in predictive microbiology is a data driven science. As such,
the experimental data (and its uncertainty) has an influence on the final predictions and …

The field of optimal experimental design uses mathematical techniques to determine
experiments that are maximally informative from a given experimental setup. Here we apply …

An approach to the design of experiments is presented in the framework of bounded-error
(guaranteed) parameter estimation for nonlinear static and dynamic systems. The …

In the field of chemical engineering, mathematical models have been proven to be an
indispensable tool for process analysis, process design, and condition monitoring. To gain …

Computational process models in combination with innovative design methodologies
provide a powerful reactor design platform. Yet, model-based design is mostly done in a …

The construction of kinetic models has become an indispensable step in developing and
scale-up of processes in the industry. Model-based design of experiments (MBDoE) has …

Accurate estimation of parameters is paramount in developing high-fidelity models for
complex dynamical systems. Model-based optimal experiment design (OED) approaches …

In order to estimate parameters accurately in nonlinear dynamic systems, experiments that
yield a maximum of information are invaluable. Such experiments can be obtained by …

Optimal design of experiments for Bayesian inverse problems has recently gained wide
popularity and attracted much attention, especially in the computational science and …

Nonlinear model predictive control (NMPC) has become an important tool for optimization
based control of many (bio) chemical systems. A requirement for a well-performing NMPC …