The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …

Dynamic mode decomposition (DMD) provides a regression framework for adaptively
learning a best-fit linear dynamics model over snapshots of temporal, or spatio-temporal …

Time-delay embedding and dimensionality reduction are powerful techniques for
discovering effective coordinate systems to represent the dynamics of physical systems …

Desert locusts threaten crop production and food security. Spatially synchronized locust
outbreaks further exacerbate these crises. Continental-scale understanding of such …

The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman (HJB)
equation, are ubiquitous in reinforcement learning (RL) and control theory. However, these …

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 …

Concern for climate change is driving a vastly increased use of electricity and variable
renewable energy supply encourages larger and evermore interconnected power systems …

Dynamic mode decomposition (DMD) is widely used for extracting dominant structures of
unsteady flow fields. However, the traditional mode time coefficient of DMD is assumed to …

With the increase in collected data volumes, either from experimental measurements or high
fidelity simulations, there is an ever-growing need to develop computationally efficient tools …

The application of time-domain solvers for characterizing high-quality () factor
electromagnetic (EM) cavities enables the acquisition of broadband data from a single run …