SEDAN fuses graph-based urban semantics and spatial structure inside a conditional diffusion model to generate behaviorally plausible and geographically coherent OD matrices, reporting a 7.38% RMSE gain over the WEDAN baseline on U.S. city data.
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Conditional flow matching with sparsity-aware gradient guidance recovers interior granular kinematics and stresses from as little as 16% boundary coverage or 11% spatial data, beating a CNN baseline in the most ill-posed regime.
A diffusion model with hierarchical physics-feature conditioning and a thermal-conduction-inspired connectivity loss reduces compliance error and floating material in topology optimization.
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Fusing Urban Structure and Semantics: A Conditional Diffusion Model for Cross-City OD Matrix Generation
SEDAN fuses graph-based urban semantics and spatial structure inside a conditional diffusion model to generate behaviorally plausible and geographically coherent OD matrices, reporting a 7.38% RMSE gain over the WEDAN baseline on U.S. city data.
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Generative modeling of granular flow on inclined planes using conditional flow matching
Conditional flow matching with sparsity-aware gradient guidance recovers interior granular kinematics and stresses from as little as 16% boundary coverage or 11% spatial data, beating a CNN baseline in the most ill-posed regime.
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HPG-Diff: Hierarchical physics-guided diffusion with differentiable connectivity constraints for topology optimization
A diffusion model with hierarchical physics-feature conditioning and a thermal-conduction-inspired connectivity loss reduces compliance error and floating material in topology optimization.
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