This paper compares descriptor models and geometric point graph neural networks for structural response prediction in procedurally generated L-shaped brackets with zero to eight holes. Complexity is quantified using hole count, removed-area fraction, boundary multiplier, inverse compactness and normalized minimum ligam...
Pancho Dachkinov, Tanio Tanev· Zenodo (CERN European Organi...· 0 citations
This paper compares descriptor models and geometric point graph neural networks for structural response prediction in procedurally generated L-shaped brackets with zero to eight holes. Complexity is quantified using hole count, removed-area fraction, boundary multiplier, inverse compactness and normalized minimum ligam...
Pancho Dachkinov, Tanio Tanev· Zenodo (CERN European Organi...· 0 citations
This paper compares descriptor models and geometric point graph neural networks for structural response prediction in procedurally generated L-shaped brackets with zero to eight holes. Complexity is quantified using hole count, removed-area fraction, boundary multiplier, inverse compactness and normalized minimum ligam...
Pancho Dachkinov, Tanio Tanev· Zenodo (CERN European Organi...· 0 citations
This paper compares descriptor models and geometric point graph neural networks for structural response prediction in procedurally generated L-shaped brackets with zero to eight holes. Complexity is quantified using hole count, removed-area fraction, boundary multiplier, inverse compactness and normalized minimum ligam...
Pancho Dachkinov, Tanio Tanev· Zenodo (CERN European Organi...· 0 citations
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