Machine learning in aerodynamic shape optimization
Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …
optimization (ASO), thanks to the availability of aerodynamic data and continued …
Data‐Driven Design for Metamaterials and Multiscale Systems: A Review
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 …
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
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 …
Synthesizing controlled microstructures of porous media using generative adversarial networks and reinforcement learning
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 …
twins. They provide stochastic samples upon which direct numerical simulations can be …
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 …
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 …
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 …
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
Generative adversarial networks (GANs) have shown remarkable success in various
generative design tasks, from topology optimization to material design, and shape …
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 …
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 …
In this research, the data-driven acceleration of an iterative inverse design method was …