GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.
Geometry-Informed Neural Networks
3 Pith papers cite this work, alongside 1 external citations. Polarity classification is still indexing.
3
Pith papers citing it
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external citations · Pith
abstract
Geometry is a ubiquitous tool in computer graphics, design, and engineering. However, the lack of large shape datasets limits the application of state-of-the-art supervised learning methods and motivates the exploration of alternative learning strategies. To this end, we introduce geometry-informed neural networks (GINNs) -- a framework for training shape-generative neural fields without data by leveraging user-specified design requirements in the form of objectives and constraints. By adding diversity as an explicit constraint, GINNs avoid mode-collapse and can generate multiple diverse solutions, often required in geometry tasks. Experimentally, we apply GINNs to several problems spanning physics, geometry, and engineering design, showing control over geometrical and topological properties, such as surface smoothness or the number of holes. These results demonstrate the potential of training shape-generative models without data, paving the way for new generative design approaches without large datasets.
representative citing papers
GIBLy is an architecture-agnostic lightweight layer that adds learnable geometric priors aligned with simple shapes to 3D semantic segmentation pipelines, delivering consistent mIoU gains (up to +11.5% on TS40K) with only 58K extra parameters.
NOTES couples a DeepONet topology decoder with CMA-ES in a PCA-derived latent space, achieving >95% deflection efficiency on nanophotonic metagratings and compliance of 246 on MBB beams, outperforming direct CMA-ES and gradient-based baselines.
citing papers explorer
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GLUE: Coordinating Pre-Trained Generative Models for System-Level Design
GLUE orchestrates frozen pre-trained generative models into a system-level design generator that enforces feasibility, performance, and diversity, with data-driven and data-free variants benchmarked on UAV design.
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GIBLy: Improving 3D Semantic Segmentation through an Architecture-Agnostic Lightweight Geometric Inductive Bias Layer
GIBLy is an architecture-agnostic lightweight layer that adds learnable geometric priors aligned with simple shapes to 3D semantic segmentation pipelines, delivering consistent mIoU gains (up to +11.5% on TS40K) with only 58K extra parameters.
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Neural Operator-enabled Topology-informed Evolutionary Strategy for PDE-Constrained Optimization
NOTES couples a DeepONet topology decoder with CMA-ES in a PCA-derived latent space, achieving >95% deflection efficiency on nanophotonic metagratings and compliance of 246 on MBB beams, outperforming direct CMA-ES and gradient-based baselines.