Evocube: A Genetic Labelling Framework for Polycube‐Maps

Polycube-maps are used as base-complexes in various fields of computational geometry, including the generation regular all-hexahedral meshes free internal singularities. However, strict alignment constraints behind polycube-based methods make their computation challenging for CAD models numerical simulation via finite element method (FEM). We propose a novel approach based on an evolutionary algorithm to robustly compute polycube-maps this context. address labelling problem, which aims precompute polycube by assigning one base axes each boundary face input. Previous research has described ways initialize and improve greedy local fixes. such algorithms lack robustness often converge inaccurate solutions complex geometries. Our proposed framework alleviates issue embedding operations heuristic, defining fitness, crossover, mutations context optimization. evaluate our thousand smooth meshes, showing Evocube converges accurate labellings wide range shapes. The limitations also discussed thoroughly.