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

A multitude of cyber-physical system (CPS) applications, including design, control,
diagnosis, prognostics, and a host of other problems, are predicated on the assumption of …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

The integration of data and scientific computation is driving a paradigm shift across the
engineering, natural, and physical sciences. Indeed, there exists an unprecedented …

Soft robots promise improved safety and capability over rigid robots when deployed near
humans or in complex, delicate, and dynamic environments. However, infinite degrees of …

This paper presents a class of linear predictors for nonlinear controlled dynamical systems.
The basic idea is to lift (or embed) the nonlinear dynamics into a higher dimensional space …

Controlling soft robots with precision is a challenge due to the difficulty of constructing
models that are amenable to model-based control design techniques. Koopman operator …

Spectral decomposition of the Koopman operator is attracting attention as a tool for the
analysis of nonlinear dynamical systems. Dynamic mode decomposition is a popular …

Optimal sensor and actuator placement is an important unsolved problem in control theory.
Nearly every downstream control decision is affected by these sensor and actuator …

Koopman operators are of infinite dimension and capture the characteristics of nonlinear
dynamics in a lifted global linear manner. The finite data-driven approximation of Koopman …