Towards Goal, Feasibility, and Diversity-Oriented Deep Generative Models in Design

by   Lyle Regenwetter, et al.

Deep Generative Machine Learning Models (DGMs) have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. DGMs are conventionally trained to minimize statistical divergence between the distribution over generated data and distribution over the dataset on which they are trained. While sufficient for the task of generating "realistic" fake data, this objective is typically insufficient for design synthesis tasks. Instead, design problems typically call for adherence to design requirements, such as performance targets and constraints. Advancing DGMs in engineering design requires new training objectives which promote engineering design objectives. In this paper, we present the first Deep Generative Model that simultaneously optimizes for performance, feasibility, diversity, and target achievement. 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. Methods are tested on a challenging multi-objective bicycle frame design problem with skewed, multimodal data of different datatypes. The proposed framework was found to outperform all Deep Generative Models in six of eight metrics.


page 1

page 2

page 3

page 4


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

Deep generative models, such as Variational Autoencoders (VAEs), Generat...

A Meta-Generation framework for Industrial System Generation

Generative design is an increasingly important tool in the industrial wo...

Multi-objective Deep Data Generation with Correlated Property Control

Developing deep generative models has been an emerging field due to the ...

Divergence Frontiers for Generative Models: Sample Complexity, Quantization Level, and Frontier Integral

The spectacular success of deep generative models calls for quantitative...

Learning from Invalid Data: On Constraint Satisfaction in Generative Models

Generative models have demonstrated impressive results in vision, langua...

Creative divergent synthesis with generative models

Machine learning approaches now achieve impressive generation capabiliti...

Please sign up or login with your details

Forgot password? Click here to reset