Johannes Markmiller, "Quantification and Optimization of a Structural Health Monitoring Sytstem for Impact Detection in Composite Structures".
The importance of uncertainty quantification and optimization techniques for producing improved models and designs is thoroughly discussed.
For this reason it becomes practically impossible to perform a robust and comprehensive analysis that requires multiple simulations such as sensitivity analysis, uncertainty quantification and optimization.
However, despite being administered concurrently, MBs, therapeutic agent, and contrast agent have distinct pharmacodynamic behaviors, thus complicating the quantification and optimization of BBB-opening and drug delivery.
Moreover, the sensitivity, selectivity, and detection limit have the potential to be improved by further quantification and optimization of the determination conditions, such as thickness of the imprinted layer, ratio of CDs to CdSe QDs, ratio of template to monomer, incubation time, and pH [10].
We present an original framework for uncertainty quantification (UQ) in optimization.
Recently, the combination with tools for uncertainty quantification, inverse modelling, optimization and control started a development towards what we call extended simulation frameworks.
In order to address such a problem, this work proposes a methodology, based on Kriging models, to efficiently assess the uncertainty quantification in the optimization process.
In this context, advanced uncertainty quantification and robust optimization methodologies are nowadays available and could be used during the ORC turbine design process in order to account for multiple uncertainties.
Amidi, M. et al. Optimization and quantification of protein synthesis inside liposomes.
