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

A data-driven framework is proposed towards the end of predictive modeling of complex
spatio-temporal dynamics, leveraging nested non-linear manifolds. Three levels of neural …

Most modeling approaches lie in either of the two categories: physics‐based or data‐driven.
Recently, a third approach which is a combination of these deterministic and statistical …

Koopman decomposition is a nonlinear generalization of eigen-decomposition, and is being
increasingly utilized in the analysis of spatio-temporal dynamics. Well-known techniques …

We propose a local, non-intrusive model order reduction technique to accurately
approximate the solution of coupled multi-component parametrized systems governed by …

Large-scale engineering systems, such as propulsive engines, ship structures, and wind
farms, feature complex, multi-scale interactions between multiple physical phenomena …

We propose a model order reduction technique to accurately approximate the behavior of
multi‐component systems without any a‐priori knowledge of the coupled model. In the …

The impact of chemical reactions on the robustness and accuracy of projection-based
reduced-order models (ROMs) of fluid flows is investigated. Both Galerkin and least squares …

A multiple neural network controller is proposed and demonstrated to suppress the pressure
oscillation of the Rijke tube acoustic network. This controller consists of three modules …

Hybrid physics-machine learning models are increasingly being used in simulations of
transport processes. Many complex multiphysics systems relevant to scientific and …