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encoded memory 1.0 _ machine learning Workshop
05 BENCH PARAMETRIC MODEL (12 DIMANSIONAL TENSOR) Bench models consiting of 12 (twelve) parameters forms an input for supervised machine learning. Both input designs are built from the same type of parameters with different values representing boundry conditions. The 12 parameters are : 1. inner diameter 2. open / closed circle for seating (controlled by 2 parameters) 3. continuity / fractioning (number of divisions within the bench) 4. height of the seat 5. depth of the seat 6. round / sharp edges of the seat 7. thickness of the backrest 8. backrest height 9. positioning of the backrest towards inner or outer radius (controlled by 2 parameters) 10. circular / polygonal shape controlled by sectioning “resolution�
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input geometry perspectives
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