Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Regenwetter, Lyle"'
This paper introduces a public dataset of 1.4 million procedurally-generated bicycle designs represented parametrically, as JSON files, and as rasterized images. The dataset is created through the use of a rendering engine which harnesses the BikeCAD
Externí odkaz:
http://arxiv.org/abs/2402.05301
Topology optimization is a critical task in engineering design, where the goal is to optimally distribute material in a given space for maximum performance. We introduce Neural Implicit Topology Optimization (NITO), a novel approach to accelerate top
Externí odkaz:
http://arxiv.org/abs/2402.05073
Generative models have demonstrated impressive results in vision, language, and speech. However, even with massive datasets, they struggle with precision, generating physically invalid or factually incorrect data. This is particularly problematic whe
Externí odkaz:
http://arxiv.org/abs/2306.15166
Designers may often ask themselves how to adjust their design concepts to achieve demanding functional goals. To answer such questions, designers must often consider counterfactuals, weighing design alternatives and their projected performance. This
Externí odkaz:
http://arxiv.org/abs/2305.11308
Autor:
Regenwetter, Lyle
This treatise explores the application of Deep Generative Machine Learning Models to bicycle design and optimization. Deep Generative Models have been growing in popularity across the design community thanks to their ability to learn and mimic comple
Externí odkaz:
https://hdl.handle.net/1721.1/144624
Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of applications, including image and speech synthesis, natural language
Externí odkaz:
http://arxiv.org/abs/2302.02913
Autor:
Regenwetter, Lyle, Ahmed, Faez
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 betwe
Externí odkaz:
http://arxiv.org/abs/2206.07170
Autor:
Regenwetter, Lyle, Ahmed, Faez
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. While early works are promising, further advancement will depend on addressing
Externí odkaz:
http://arxiv.org/abs/2205.03005
This paper demonstrates how Automated Machine Learning (AutoML) methods can be used as effective surrogate models in engineering design problems. To do so, we consider the challenging problem of structurally-performant bicycle frame design and demons
Externí odkaz:
http://arxiv.org/abs/2201.10459
Automated design synthesis has the potential to revolutionize the modern engineering design process and improve access to highly optimized and customized products across countless industries. Successfully adapting generative Machine Learning to desig
Externí odkaz:
http://arxiv.org/abs/2110.10863