In the Methodology of Experimental Research (M.E.R.), the quality of the results depends on the choice of the experimental design. A lot of experimental designs exist. The goal of the presentation is to optimize the choice of an experimental design by neural networks. We present in detail the elaboration of networks which facilitates the choice of an experimental design as a second-degree model...

The geosciences are a highly suitable field of application for optimizing model parameters and experimental designs especially because many data are collected. In this paper, the weighted least squares estimator for optimizing model parameters is presented together with its asymptotic properties. A popular approach to optimize experimental designs called local optimal experimental designs is de...

Mathematical models are expected to play a pivotal role for driving microalgal production towards a profitable process of renewable energy generation. To render models of microalgae growth useful tools for prediction and process optimization, reliable parameters need to be provided. This reliability implies a careful design of experiments that can be exploited for parameter estimation. In this ...

Designs for nonlinear regression models depend on some prior information about the unknown parameters. There are three primary methods for accounting for this: The locally optimal designs, globally optimal Bayesian designs, and sequential procedures. If prior knowledge about the parameters is available from former experiments, Bayesian designs integrate this information most eeciently. If the e...

Inferring the causal structure of a system typically requires interventional data, rather than just observational data. Since experiments can be costly, it is preferable to select interventions that yield maximum amount information about system. We propose novel Bayesian method for optimal experimental design by sequentially selecting minimize expected posterior entropy as rapidly possible. A k...