28 citations · 40 across the 11 of their papers we have counts for
5 papers · 1 filter
Stylish and Functional: Guided Interpolation Subject to Physical Constraints
Yan-Ying Chen, Nikos Arechiga, Chenyang Yuan +3
Generative AI is revolutionizing engineering design practices by enabling rapid prototyping and manipulation of designs. One example of design manipulation involves taking two refe…
Bridging Design Gaps: A Parametric Data Completion Approach With Graph Guided Diffusion Models
Rui Zhou, Chenyang Yuan, Frank Permenter +4
This study introduces a generative imputation model leveraging graph attention networks and tabular diffusion models for completing missing parametric data in engineering designs.…
Drag-guided diffusion models for vehicle image generation
Nikos Arechiga, Frank Permenter, Binyang Song +1
Denoising diffusion models trained at web-scale have revolutionized image generation. The application of these tools to engineering design is an intriguing possibility, but is curr…
Interpreting and Improving Diffusion Models from an Optimization Perspective
Frank Permenter, Chenyang Yuan
Denoising is intuitively related to projection. Indeed, under the manifold hypothesis, adding random noise is approximately equivalent to orthogonal perturbation. Hence, learning t…
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings
Binyang Song, Chenyang Yuan, Frank Permenter +2
Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization…