Deep generative models in engineering design: A review
L Regenwetter, AH Nobari… - Journal of …, 2022 - asmedigitalcollection.asme.org
Automated design synthesis has the potential to revolutionize the modern engineering
design process and improve access to highly optimized and customized products across …
design process and improve access to highly optimized and customized products across …
Multi-modal machine learning in engineering design: A review and future directions
In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of
multiple data modalities has the potential to reshape various applications. This paper …
multiple data modalities has the potential to reshape various applications. This paper …
[HTML][HTML] ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model
In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep
convolutional generative adversarial networks (GANs) for the versatile representation and …
convolutional generative adversarial networks (GANs) for the versatile representation and …
Generative design by reinforcement learning: enhancing the diversity of topology optimization designs
Generative design refers to computational design methods that can automatically conduct
design exploration under constraints defined by designers. Among many approaches …
design exploration under constraints defined by designers. Among many approaches …
Aligning optimization trajectories with diffusion models for constrained design generation
G Giannone, A Srivastava… - Advances in Neural …, 2024 - proceedings.neurips.cc
Generative models have significantly influenced both vision and language domains,
ushering in innovative multimodal applications. Although these achievements have …
ushering in innovative multimodal applications. Although these achievements have …
Beyond statistical similarity: Rethinking metrics for deep generative models in engineering design
Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial
Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a …
Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a …
From concept to manufacturing: Evaluating vision-language models for engineering design
Engineering Design is undergoing a transformative shift with the advent of AI, marking a new
era in how we approach product, system, and service planning. Large language models …
era in how we approach product, system, and service planning. Large language models …
t-metaset: Task-aware acquisition of metamaterial datasets through diversity-based active learning
Inspired by the recent achievements of machine learning in diverse domains, data-driven
metamaterials design has emerged as a compelling paradigm that can unlock the potential …
metamaterials design has emerged as a compelling paradigm that can unlock the potential …
Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network
The core challenge in directed energy deposition is to obtain high surface quality through
process optimisation, which directly affects the mechanical properties of fabricated parts …
process optimisation, which directly affects the mechanical properties of fabricated parts …
[HTML][HTML] Adjoint method in machine learning: a pathway to efficient inverse design of photonic devices
Innovative machine learning techniques have facilitated the inverse design of photonic
structures for numerous practical applications. Nevertheless, the quantity of data and the …
structures for numerous practical applications. Nevertheless, the quantity of data and the …