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Found 10 papers, 2 papers with code
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.
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.
Learning from Invalid Data: On Constraint Satisfaction in Generative Models
This is particularly problematic when the generated data must satisfy constraints, for example, to meet product specifications in engineering design or to adhere to the laws of physics in a natural scene.
Counterfactuals for Design: A Model-Agnostic Method For Design Recommendations
In this paper, the authors frame the counterfactual search problem as a design recommendation tool that can help identify modifications to a design, leading to better functional performance.
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design
This paper doubles as a review and a practical guide to evaluation metrics for deep generative models (DGMs) in engineering design.
Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design
We benchmark performance of the proposed method against several Deep Generative Models over eight evaluation metrics that focus on feasibility, diversity, and satisfaction of design performance targets.
Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design
Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions.
FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames
Our work introduces a dataset for bicycle design practitioners, provides two benchmark problems for surrogate modeling researchers, and demonstrates the advantages of AutoML in machine learning tasks.
Deep Generative Models in Engineering Design: A Review
We present a review and analysis of Deep Generative Machine Learning models in engineering design.
BIKED: A Dataset for Computational Bicycle Design with Machine Learning Benchmarks
In this paper, we present "BIKED," a dataset comprised of 4500 individually designed bicycle models sourced from hundreds of designers.
