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

The recent development of machine learning (ML) and Deep Learning (DL) increases the
opportunities in all the sectors. ML is a significant tool that can be applied across many …

Sparse model identification enables the discovery of nonlinear dynamical systems purely
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …

This paper discusses the spectral proper orthogonal decomposition and its use in identifying
modes, or structures, in flow data. A specific algorithm based on estimating the cross …

THE field of fluid mechanics involves a range of rich and vibrant problems with complex
dynamics stemming from instabilities, nonlinearities, and turbulence. The analysis of these …

SIMPLE aerodynamic configurations under even modest conditions can exhibit complex
flows with a wide range of temporal and spatial features. It has become common practice in …

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

Informed by large-eddy simulation (LES) data and resolvent analysis of the mean flow, we
examine the structure of turbulence in jets in the subsonic, transonic and supersonic …

Modal-decomposition techniques are computational frameworks based on data aimed at
identifying a low-dimensional space for capturing dominant flow features: the so-called …

The digital twin concept represents an appealing opportunity to advance condition-based
and predictive maintenance paradigms for civil engineering systems, thus allowing reduced …