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

Abstract Model building and parameter estimation are traditional concepts widely used in
chemical, biological, metallurgical, and manufacturing industries. Early modeling …

Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …

There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …

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 …

Unsteady fluid systems are nonlinear high-dimensional dynamical systems that may exhibit
multiple complex phenomena in both time and space. Reduced Order Modeling (ROM) of …

The large and catastrophic wildfires have been increasing across the globe in the recent
decade, highlighting the importance of simulating and forecasting fire dynamics in near real …

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven
reduced order modeling of dynamical systems relevant to fluid flows. We propose various …

Traditional data-driven deep learning models often struggle with high training costs, error
accumulation, and poor generalizability in complex physical processes. Physics-informed …

Reduced-order modelling and low-dimensional surrogate models generated using machine
learning algorithms have been widely applied in high-dimensional dynamical systems to …