Sentence examples for multivariate metamodelling allowed from inspiring English sources

The multivariate metamodelling allowed a wide set of nonlinear curvature descriptions to be handled.

Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation.

This has here been illustrated for the nine input parameters assumed to be most interesting for the mammalian circadian clock and the 16 state variables of the model, where the generated N-way metamodels allowed flexible quantitative input-output regressions yielding informative graphical insight into the main underlying input-output map characteristics.

Here, we combine three different aspects of multivariate metamodelling: 1) Description of highly nonlinear input-output relationships by regional metamodelling, 2) NPLSR, allowing a retention of a tensor data structure throughout the analysis and 3) Inverse metamodelling in addition to the classical approach, providing more confident conclusions and a more comprehensive model overview.

Thus, a first version of a complete "modelome" of the mathematical phenomenon "line curvature" has been established by multivariate metamodelling and described in terms of quantitative maps both to the original model parameters in the 38 individual models and to human verbal description of curve shapes.

A more comprehensive introduction to the multivariate metamodelling methodology is given in Additional file 1: Section S1.

As long as they handle high-dimensional data with nonlinear relationships and yield interpretable representations, a wide variety of statistical methods can be effectively used for multivariate metamodelling.

Multivariate metamodelling has, at least in principle, the capacity to reveal the relationships between all input parameters and all model outputs simultaneously.

In our opinion, this methodology should be considered as particularly useful in future multivariate metamodelling for analysis of complex spatiotemporal models.

Metamodelling has been widely used in e.g. engineering, for speed-up of computations, sensitivity analysis and uncertainty assessment [ 37], and recently, multivariate metamodelling using PLSR [ 25- 28, 30, 38] and HC-PLSR [ 5, 6, 29] has been shown to be effective for analysis of the complex, nonlinear input output relationships of biological models.

Due to its efficient handling of N-way data structures, demonstrated here in the analysis of the temporal model behaviour, we hypothesise that N-way HC-PLSR will be an especially useful tool for multivariate metamodelling of spatiotemporal models, a large future application area.