### 5.5.1. Algorithms

The General Interpolation Library (GIL) currently provides 3 interpolation algorithms:

- Nearest neighbor

- Linear multivariate

- Radial basis

The GIL is a suitable library for scattered data interpolation. For example, consider a set of materials testing results that represent the Young's modulus of a material at different temperatures and humidities. The test series will contain only a few combinations of temperature and humidity at which an actual measurement was made. When transferring this testing data to Workbench Engineering Data and performing an analysis making use of this material, it is expected that Workbench makes reasonable guesses at the Young's modulus at intermediate combinations of temperature and humidity as the simulation is performed.

Generally, the GIL interpolates a dependent variable, which is only known at a few scattered points in the n-dimensional space of the independent (defining) variables:

is the interpolation function. The set of points in the independent data space together with their given dependent variable values are called supporting points. The point in the independent space for which the dependent value is requested is called the query point.

The GIL has no restriction on the dimensions of the independent data space. However, composite analyses with variable material data are currently restricted to 9 dimensions. This means that properties such as Young's modulus can be controlled by up to 9 variables.

Note: The GIL is only suitable for interpolation of scalar quantities.

The GIL limitations are listed in Known Limitations.

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