Dynamic Mode Decomposition (DMD) is a popular data-driven analysis technique used to
decompose complex, nonlinear systems into a set of modes, revealing underlying patterns …

Recently developed reduced-order modeling techniques aim to approximate nonlinear
dynamical systems on low-dimensional manifolds learned from data. This is an effective …

Dynamic mode decomposition (DMD) and its variants, such as extended DMD (EDMD), are
broadly used to fit simple linear models to dynamical systems known from observable data …

Computing reduced-order models using non-intrusive methods is particularly attractive for
systems that are simulated using black-box solvers. However, obtaining accurate data …

Eigenvectors of the observability and controllability Gramians represent responsive and
receptive flow structures that enjoy a well-established connection to resolvent forcing and …

During the last decade, forcing and response modes produced by resolvent analysis have
demonstrated great potential to guide sensor and actuator placement and design in flow …

State-specific thermochemical collisional models are crucial to accurately describe the
physics of systems involving nonequilibrium plasmas, but they are also computationally …

The ability to measure differences in collected data is of fundamental importance for
quantitative science and machine learning, motivating the establishment of metrics …

There is a mystery at the heart of operator learning: how can one recover a non-self-adjoint
operator from data without probing the adjoint? Current practical approaches suggest that …

We propose a method to design reduced-order output-feedback controllers for fluid flows
with the use of data produced by full-order controllers. First, the full-order controller is …