{"ID":2881237,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.13097","arxiv_id":"2508.13097","title":"Denoising diffusion models for inverse design of inflatable structures with programmable deformations","abstract":"Programmable structures are systems whose undeformed geometries and material property distributions are deliberately designed to achieve prescribed deformed configurations under specific loading conditions. Inflatable structures are a prominent example, using internal pressurization to realize large, nonlinear deformations in applications ranging from soft robotics and deployable aerospace systems to biomedical devices and adaptive architecture. We present a generative design framework based on denoising diffusion probabilistic models (DDPMs) for the inverse design of elastic structures undergoing large, nonlinear deformations under pressure-driven actuation. The method formulates the inverse design as a conditional generation task, using geometric descriptors of target deformed states as inputs and outputting image-based representations of the undeformed configuration. Representing these configurations as simple images is achieved by establishing a pre- and postprocessing pipeline that involves a fixed image processing, simulation setup, and descriptor extraction methods. Numerical experiments with scalar and higher-dimensional descriptors show that the framework can quickly produce diverse undeformed configurations that achieve the desired deformations when inflated, enabling parallel exploration of viable design candidates while accommodating complex constraints.","short_abstract":"Programmable structures are systems whose undeformed geometries and material property distributions are deliberately designed to achieve prescribed deformed configurations under specific loading conditions. Inflatable structures are a prominent example, using internal pressurization to realize large, nonlinear deformat...","url_abs":"https://arxiv.org/abs/2508.13097","url_pdf":"https://arxiv.org/pdf/2508.13097v1","authors":"[\"Sara Karimi\",\"Nikolaos N. Vlassis\"]","published":"2025-08-18T17:07:51Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.LG\"]","methods":"[\"Diffusion Model\",\"LoRA\"]","has_code":false}