{"ID":2858467,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.08830","arxiv_id":"2510.08830","title":"Data-driven multifidelity and multiscale topology optimization based on phasor-based evolutionary de-homogenization","abstract":"Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear settings. Moreover, the resulting hierarchical geometries are often overly complex and lack macroscopically meaningful features. To overcome these issues, we propose an evolutionary de-homogenization framework that couples MultiFidelity Topology Design (MFTD) with a phasor-based de-homogenization technique. The framework translates low-dimensional geometric descriptors into manufacturable high-resolution structures through a hybrid evolutionary algorithm integrating NSGA-II selection, VAE-enabled latent space crossover, and a novel image deformation-based mutation operator. This gradient-free approach achieves efficient optimization while ensuring geometric continuity. Numerical results confirm that the method effectively balances efficiency and design flexibility, offering a scalable pathway for fabrication-aware multiscale structural optimization.","short_abstract":"Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear setting...","url_abs":"https://arxiv.org/abs/2510.08830","url_pdf":"https://arxiv.org/pdf/2510.08830v1","authors":"[\"Shuzhi Xu\",\"Yifan Guo\",\"Hiroki Kawabe\",\"Kentaro Yaji\"]","published":"2025-10-09T21:32:17Z","proceeding":"math.OC","tasks":"[\"math.OC\"]","methods":"[\"Variational Autoencoder\"]","has_code":false}