Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design-Reference-Cited by-同舟云学术

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Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design

Published:2023-12 Issue: Volume:165 Page:103609
ISSN:0010-4485
Container-title:Computer-Aided Design
language:en
Short-container-title:Computer-Aided Design

Author:

Regenwetter Lyle^ORCID,Srivastava Akash,Gutfreund Dan,Ahmed Faez

Funder

MIT-IBM Watson AI Lab

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Graphics and Computer-Aided Design,Computer Science Applications

Reference102 articles.

1. Generative adversarial nets;Goodfellow,2014

2. Auto-encoding variational bayes;Kingma,2013

3. Deep generative models in engineering design: A review;Regenwetter;J Mech Des,2022

4. Diffusion models in vision: A survey;Croitoru,2022

5. Diffusion models: A comprehensive survey of methods and applications;Yang,2022

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-Objective Counterfactuals for Design: A Model-Agnostic Counterfactual Search Method for Multi-Modal Design Modifications;Journal of Mechanical Design;2024-08-28

2. A Meta-VAE for Multi-component Industrial Systems Generation;Lecture Notes in Networks and Systems;2024

3. Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions;Journal of Computing and Information Science in Engineering;2023-11-24

4. ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints;Journal of Marine Science and Engineering;2023-11-22

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