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 …
Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: A state-of-the-art overview
During the past few decades, several significant progresses have been made in exploring
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
[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 …
Airfoil design and surrogate modeling for performance prediction based on deep learning method
Q Du, T Liu, L Yang, L Li, D Zhang, Y **e - Physics of Fluids, 2022 - pubs.aip.org
Airfoil design and surrogate modeling for performance prediction based on deep learning
method | Physics of Fluids | AIP Publishing Skip to Main Content Umbrella Alt Text Umbrella Alt …
method | Physics of Fluids | AIP Publishing Skip to Main Content Umbrella Alt Text Umbrella Alt …
Padgan: Learning to generate high-quality novel designs
Deep generative models are proven to be a useful tool for automatic design synthesis and
design space exploration. When applied in engineering design, existing generative models …
design space exploration. When applied in engineering design, existing generative models …
On deep-learning-based geometric filtering in aerodynamic shape optimization
Geometric filtering based on deep-learning models has been shown to be effective to shrink
the design space and improve the efficiency of aerodynamic shape optimization. However …
the design space and improve the efficiency of aerodynamic shape optimization. However …
Engineering sketch generation for computer-aided design
Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the
foremost modeling paradigm for manufactured objects. In this paper we tackle the problem …
foremost modeling paradigm for manufactured objects. In this paper we tackle the problem …
Enhanced data efficiency using deep neural networks and Gaussian processes for aerodynamic design optimization
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily
due to their computational costs being independent of the dimensionality of the input space …
due to their computational costs being independent of the dimensionality of the input space …
Evolutionary generative design of supercritical airfoils: an automated approach driven by small data
Supercritical airfoils are critical components in the design of commercial wide-body aircraft
wings due to their ability to enhance aerodynamic performance in transonic flow regimes …
wings due to their ability to enhance aerodynamic performance in transonic flow regimes …