Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …

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

This work presents a review and perspectives on recent developments in the use of machine
learning (ML) to augment Reynolds-averaged Navier-Stokes (RANS) and large eddy …

Aerodynamic modeling plays an important role in multiphysics and design problems, in
addition to experiment and numerical simulation, due to its low-dimensional representation …

We present a new nonlinear mode decomposition method to visualize decomposed flow
fields, named the mode decomposing convolutional neural network autoencoder (MD-CNN …

A common strategy for the dimensionality reduction of nonlinear partial differential equations
(PDEs) relies on the use of the proper orthogonal decomposition (POD) to identify a reduced …

In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …

Data science, and machine learning in particular, is rapidly transforming the scientific and
industrial landscapes. The aerospace industry is poised to capitalize on big data and …