Results 21 to 30 of about 405,858 (264)
Construction Economics and Building, 2021 Digital fabrication leads architects and structural engineers to modify the design optimisation methodology. The designers, as never before, are facing new technologies developed in the search for new materials based, among others, on wood components and
Anna Stefańska +3 more
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Incertitudes et Fiabilité des Systèmes MultiphysiquesAdditive manufacturing, has shown great promises, offering unexpectable design freedom and manufacturing flexibility. but traditional design methodologies cannot fully exploit the functionality of am. this study explores the impact of generative
Omar Lkadi +2 more
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Generative Creativity: Adversarial Learning for Bionic Design
, 2018 Bionic design refers to an approach of generative creativity in which a target object (e.g. a floor lamp) is designed to contain features of biological source objects (e.g. flowers), resulting in creative biologically-inspired design.
Dong, Hao +4 more
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DesIGN: Design Inspiration from Generative Networks [PDF]
, 2019 Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation.
Sbai, Othman +4 more
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BuildingsMultimodal generative AI and generative design empower architects to create better-performing, sustainable, and efficient design solutions and explore diverse design possibilities.
Adam Fitriawijaya, Taysheng Jeng
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How generative AI supports human in conceptual design
Design ScienceGenerative Artificial Intelligence (Generative AI) is a collection of AI technologies that can generate new information such as texts and images. With its strong capabilities, Generative AI has been actively studied in creative design processes. However,
Liuqing Chen +5 more
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Generative vs. Non-Generative Models in Engineering Shape Optimization
Journal of Marine Science and EngineeringGenerative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be
Zahid Masood +4 more
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Partitioning around medoids as a systematic approach to generative design solution space reduction
Results in Engineering, 2022 This study explores an approach to generative design solution space reduction by offering a flexible, efficient, and accessible method by leveraging clustering techniques.
Michael Botyarov, Erika E. Miller
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Architectural authorship in generative design [PDF]
, 2007 The emergence of evolutionary digital design methods, relying on the creative generation of novel forms, has transformed the design process altogether and consequently the role of the architect.
Theodoropoulou, A.
core
Sliced Wasserstein Generative Models
, 2019 In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is challenging to approximate the WD of high-dimensional distributions.
Acharya, Dinesh +6 more
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