An Automatic Isotropic Triangular Grid Generation Technique Based on an Artificial Neural Network and an Advancing Front Method

Table 2

Unnormalized training dataset of a typical edge (or front).

Input								Output
										Type
3.0902	9.5106	0.5864	4.9156	−2.0834	6.0788	−3.0902	9.5106	−2.4626	0.8065	1
3.0902	9.5106	0.5864	4.9156	−2.0834	6.0788	−8.0902	5.8779	−2.4626	0.8065	1
3.0902	9.5106	0.5864	4.9156	−2.0834	6.0788	−2.4626	0.8065	−2.4626	0.8065	3
3.8014	3.2664	0.5864	4.9156	−2.0834	6.0788	3.0902	9.5106	−2.4626	0.8065	1
3.8014	3.2664	0.5864	4.9156	−2.0834	6.0788	−3.0902	9.5106	−2.4626	0.8065	1
3.8014	3.2664	0.5864	4.9156	−2.0834	6.0788	−8.0902	5.8779	−2.4626	0.8065	1
3.8014	3.2664	0.5864	4.9156	−2.0834	6.0788	−2.4626	0.8065	−2.4626	0.8065	3
−2.4626	0.8065	0.5864	4.9156	−2.0834	6.0788	3.0902	9.5106	−2.4626	0.8065	2
−2.4626	0.8065	0.5864	4.9156	−2.0834	6.0788	−3.0902	9.5106	−2.4626	0.8065	2
−2.4626	0.8065	0.5864	4.9156	−2.0834	6.0788	−8.0902	5.8779	−2.4626	0.8065	2