![hypercube calculator hypercube calculator](https://i.ytimg.com/vi/5GB14nYS4XY/hqdefault.jpg)
The objective is to maximize this distance − This criterion is calculated using the distance between the closest two points on the design. Space-filling: the trials of the LHS DOE should fill the input domain as much as possible. The main characteristics of LHS designs are: − This design is not unique and the space-filling and independence criteria are relative. In this case, a very high number of designs are generated and the design which contains the best space-filling and independence criteria is selected.
![hypercube calculator hypercube calculator](https://ae01.alicdn.com/kf/HTB1ccNGX13tHKVjSZSgq6x4QFXa1/1Set-HyperCube-Evolution-3D-Printer-Metal-Frame-Extrusion-with-hardware-kit-X300-x-Y300-x-Z300.jpg)
LHS designs are particularly suitable for numerical designs of experiments. The objective is to maximize this determinant (if there is no correlation at all, it is equal to 1 and the design is orthogonal). This criterion is calculated using the determinant of the parameter correlation matrix. This means that they are as uncorrelated as possible (if there is correlation between two parameters A and B, it is difficult to separate the effect of A from that of B). Independence: it is desirable for the parameters to be as orthogonal as possible. The objective is to maximize this distance – Space-filling: it is desirable for the design’s test to fill the input domain as much as possible. The main properties expected for LHS designs are: –
![hypercube calculator hypercube calculator](http://i.stack.imgur.com/BG4Hy.png)
* Values in parenthesis are design values.