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 …

Abstract Machine learning (ML) has been increasingly used to aid aerodynamic shape
optimization (ASO), thanks to the availability of aerodynamic data and continued …

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 …

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 …

Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial
Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a …

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 …

In this work, we introduce ShipHullGAN, a generic parametric modeller built using deep
convolutional generative adversarial networks (GANs) for the versatile representation and …

Abstract Machine learned generative design is an extension of the generative design
process, addressing its inherent limitations, particularly those of interoperability. The …

Generative adversarial networks (GANs) have shown remarkable success in various
generative design tasks, from topology optimization to material design, and shape …

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 …