{"total":22,"rows":[{"id":"2110.10863","title":"Deep Generative Models in Engineering Design: A Review","created":"2021-10-21","cited_by_count":105,"area":null},{"id":"2305.08279","title":"Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning","created":"2023-05-14","cited_by_count":13,"area":null},{"id":"2201.10459","title":"FRAMED: An AutoML Approach for Structural Performance Prediction of Bicycle Frames","created":"2022-01-25","cited_by_count":11,"area":null},{"id":"2205.03005","title":"Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design","created":"2022-05-06","cited_by_count":6,"area":null},{"id":"2305.09018","title":"DATED: Guidelines for Creating Synthetic Datasets for Engineering Design Applications","created":"2023-05-15","cited_by_count":6,"area":null},{"id":"2508.00831","title":"EngiBench: A Framework for Data-Driven Engineering Design Research","created":"2025-06-02","cited_by_count":6,"area":null},{"id":"2305.11308","title":"MCD: A Model-Agnostic Counterfactual Search Method For Multi-modal Design Modifications","created":"2023-05-18","cited_by_count":5,"area":null},{"id":"2508.00830","title":"BikeBench: A Bicycle Design Benchmark for Generative Models with Objectives and Constraints","created":"2025-05-25","cited_by_count":5,"area":null},{"id":"2206.07170","title":"Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design","created":"2022-06-14","cited_by_count":3,"area":null},{"id":"2403.05352","title":"Fr\\'echet Denoised Distance: Enhancing Plausibility Evaluation for Generated Designs with Denoising Autoencoder","created":"2024-03-08","cited_by_count":3,"area":null},{"id":"2407.11104","title":"Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception","created":"2024-07-15","cited_by_count":3,"area":null},{"id":"2402.05301","title":"BIKED++: A Multimodal Dataset of 1.4 Million Bicycle Image and Parametric CAD Designs","created":"2024-02-07","cited_by_count":2,"area":null},{"id":"2507.12165","title":"Multi-Component VAE with Gaussian Markov Random Field","created":"2025-07-16","cited_by_count":2,"area":null},{"id":"2304.07883","title":"Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification","created":"2023-04-16","cited_by_count":1,"area":null},{"id":"2410.10782","title":"3DArticCyclists: Generating Synthetic Articulated 8D Pose-Controllable Cyclist Data for Computer Vision Applications","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2410.18358","title":"Data Publishing in Mechanics and Dynamics: Challenges, Guidelines, and Examples from Engineering Design","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2306.05123","title":"A Meta-Generation framework for Industrial System Generation","created":"2023-06-08","cited_by_count":0,"area":null},{"id":"2505.16725","title":"Masked Conditioning for Deep Generative Models","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2508.08863","title":"Flow Battery Manifold Design with Heterogeneous Inputs Through Generative Adversarial Neural Networks","created":"2025-08-12","cited_by_count":0,"area":null},{"id":"2606.12994","title":"DeepJEB++: Foundation Model-Driven Large-Scale 3D Engineering Dataset via 2D Latent Space Augmentation","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2608.24056","title":"PhysicsBench: A Unified Leaderboard for Generative and Predictive Models in Engineering Design and Simulation","created":"2026-08-25","cited_by_count":0,"area":null},{"id":"2610.02214","title":"MiDShip: Multimodal Dataset of Ship Cargo Hold Structures for Engineering Design","created":"2026-09-04","cited_by_count":0,"area":null}],"next_cursor":null}