Common purposes of simulation studies are inversion, optimization, sensitivity analysis and evaluation of performance.
In the current study, an algorithm-based data processing, sizing, optimization, sensitivity analysis and clustering approach (DaSOSaCa) is proposed as an efficient simultaneous solar/wind assessment methodology.
The computational efficiency means that methods can be integrated in iterative computational tasks such as multi-scale analysis, optimization, sensitivity analysis and simulation modelling.
We hence define two effective metrics, namely energy optimization sensitivity (EOS) and lowest operating voltage (LOV), to make our framework adapt to both of the platforms.
For example, the Computer Modelling Group Ltd has developed a CMOST studio, which integrated history matching, optimization, sensitivity analysis, and uncertainty assessment tool.
Typical uses of surrogate models include design, optimization, sensitivity analysis and/or uncertainty quantification.
In order to use out-of-the-box analysis tools (e.g. optimization, sensitivity analysis, Hamiltonian Monte Carlo), data analysts typically have to convert structured, constrained parameters (such as covariance matrices) to and from unconstrained one-dimensional vectors.
Open image in new window Fig. 10 Relationship among simulation, optimization, sensitivity.
After assay optimization, sensitivity was 6.8 copies per reaction.
The Wong Best sensitivity query scored highest on specificity (99.4%), matched by the Wong Best optimization (sensitivity > specificity) query (99.4%).
