Computational fluid dynamics (CFD) is known for producing high-dimensional
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …

Dimensionality reduction is the essence of many data processing problems, including
filtering, data compression, reduced-order modeling and pattern analysis. While traditionally …

A large-scale machine learning-based nonlinear reduced-order modeling method was
developed for a three-dimensional turbulent flow field (R e= 1000) using a neural-network …

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 …

Digital twins have emerged as a key technology for optimizing the performance of
engineering products and systems. High-fidelity numerical simulations constitute the …

We present a numerical methodology for construction of reduced-order models (ROMs) of
fluid flows through the combination of flow modal decomposition and regression analysis …

We perform a sparse identification of nonlinear dynamics (SINDy) for low-dimensionalized
complex flow phenomena. We first apply the SINDy with two regression methods, the …

We propose a unified data-driven reduced order model (ROM) that bridges the performance
gap between linear and nonlinear manifold approaches. Deep learning ROM (DL-ROM) …

We design a physics-aware auto-encoder to specifically reduce the dimensionality of
solutions arising from convection-dominated nonlinear physical systems. Although existing …

Simulating fluid flows in different virtual scenarios is of key importance in engineering
applications. However, high-fidelity, full-order models relying, eg, on the finite element …