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Found 8 papers, 6 papers with code
NITO: Neural Implicit Fields for Resolution-free Topology Optimization
We introduce Neural Implicit Topology Optimization (NITO), a novel approach to accelerate topology optimization problems using deep learning.
BIKED++: A Multimodal Dataset of 1.4 Million Bicycle Image and Parametric CAD Designs
The dataset is created through the use of a rendering engine which harnesses the BikeCAD software to generate vector graphics from parametric designs.
Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks
We demonstrate that the addition of conformal predictions effectively enables the model to avoid under-refinement, hence failure, in CFD meshing even for weak and less accurate models.
LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
LINKS is made up of various components including 100 million mechanisms, the simulation data for each mechanism, normalized paths generated by each mechanism, a curated set of paths, the code used to generate the data and simulate mechanisms, and a live web demo for interactive design of linkage mechanisms.
Deep Generative Models in Engineering Design: A Review
We present a review and analysis of Deep Generative Machine Learning models in engineering design.
PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design
Engineering design tasks often require synthesizing new designs that meet desired performance requirements.
CreativeGAN: Editing Generative Adversarial Networks for Creative Design Synthesis
GAN models, however, are not capable of generating unique designs, a key to innovation and a major gap in AI-based design automation applications.
Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesis
This work laid the foundation for data-driven inverse design problems where we consider range constraints and there are sparse regions in the condition space.
