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

Numerical simulation of large-scale dynamical systems plays a fundamental role in studying
a wide range of complex physical phenomena; however, the inherent large-scale nature of …

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 …

A reduced basis method based on a physics-informed machine learning framework is
developed for efficient reduced-order modeling of parametrized partial differential equations …

Used to analyze the time-evolution of fluid flows, dynamic mode decomposition (DMD) has
quickly gained traction in the fluids community. However, the existing DMD literature focuses …

Completely revised text focuses on use of spectral methods to solve boundary value,
eigenvalue, and time-dependent problems, but also covers Hermite, Laguerre, rational …

Turbulence pervades our world, from weather patterns to the air entering our lungs. This
book describes methods that reveal its structures and dynamics. Building on the existence of …

In today's technological world, physical and artificial processes are mainly described by
mathematical models, which can be used for simulation or control. These processes are …

This paper derives predictive reduced-order models for rocket engine combustion dynamics
via Operator Inference, a scientific machine learning approach that blends data-driven …

A hierarchy of low-dimensional Galerkin models is proposed for the viscous, incompressible
flow around a circular cylinder building on the pioneering works of Stuart (1958), Deane et …