Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding
meaningful low-dimensional structures hidden in their high-dimensional observations. Such …

Recent advances in image data proccesing through deep learning allow for new
optimization and performance-enhancement schemes for radiation detectors and imaging …

How do we capture the breadth of behavior in animal movement, from rapid body twitches to
aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we …

Every day, hundreds of thousands of people undergo general anesthesia. One hypothesis is
that anesthesia disrupts dynamic stability—the ability of the brain to balance excitability with …

Providing efficient and accurate parameterizations for model reduction is a key goal in many
areas of science and technology. Here, we present a strong link between data-driven and …

A number of recent studies have proposed that linear representations are appropriate for
solving nonlinear dynamical systems with quantum computers, which fundamentally act …

Only a subset of degrees of freedom are typically accessible or measurable in real-world
systems. As a consequence, the proper setting for empirical modeling is that of partially …

We propose to adopt statistical regression as the projection operator to enable data-driven
learning of the operators in the Mori–Zwanzig formalism. We present a principled method to …

Rapid evolution of sensor technology, advances in instrumentation, and progress in
devising data-acquisition software and hardware are providing vast amounts of data for …

Many chemical reactions and molecular processes occur on time scales that are significantly
longer than those accessible by direct simulations. One successful approach to estimating …