Machine learning in aerodynamic shape optimization

J Li, X Du, JRRA Martins - Progress in Aerospace Sciences, 2022 - Elsevier
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

D Lee, W Chen, L Wang, YC Chan… - Advanced …, 2024 - Wiley Online Library
Metamaterials are artificial materials designed to exhibit effective material parameters that
go beyond those found in nature. Composed of unit cells with rich designability that are …

[HTML][HTML] ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model

S Khan, K Goucher-Lambert, K Kostas… - Computer Methods in …, 2023 - Elsevier
In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep
convolutional generative adversarial networks (GANs) for the versatile representation and …

Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning

PCH Nguyen, NN Vlassis, B Bahmani, WC Sun… - Scientific reports, 2022 - nature.com
For material modeling and discovery, synthetic microstructures play a critical role as digital
twins. They provide stochastic samples upon which direct numerical simulations can be …

Multi-modal machine learning in engineering design: A review and future directions

B Song, R Zhou, F Ahmed - … of Computing and …, 2024 - asmedigitalcollection.asme.org
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 …

Beyond statistical similarity: Rethinking metrics for deep generative models in engineering design

L Regenwetter, A Srivastava, D Gutfreund… - Computer-Aided …, 2023 - Elsevier
Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial
Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a …

Deep learning in computational mechanics: a review

L Herrmann, S Kollmannsberger - Computational Mechanics, 2024 - Springer
The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …

Dde-gan: Integrating a data-driven design evaluator into generative adversarial networks for desirable and diverse concept generation

C Yuan, T Marion… - Journal of …, 2023 - asmedigitalcollection.asme.org
Generative adversarial networks (GANs) have shown remarkable success in various
generative design tasks, from topology optimization to material design, and shape …

[HTML][HTML] Towards machine learned generative design

L Gradišar, M Dolenc, R Klinc - Automation in Construction, 2024 - Elsevier
Abstract Machine learned generative design is an extension of the generative design
process, addressing its inherent limitations, particularly those of interoperability. The …

Machine learning-accelerated aerodynamic inverse design

A Shirvani, M Nili-Ahmadabadi… - Engineering Applications of …, 2023 - Taylor & Francis
The computational cost of iterative design methods has been a challenge in aerodynamics.
In this research, the data-driven acceleration of an iterative inverse design method was …