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

Personal and ubiquitous sensing technologies such as smartphones have allowed the
continuous collection of data in an unobtrusive manner. Machine learning methods have …

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

In this article, we discuss connections between sequential system identification and control
for linear time-invariant systems, often termed indirect data-driven control, as well as a …

We develop a new method which extends dynamic mode decomposition (DMD) to
incorporate the effect of control to extract low-order models from high-dimensional, complex …

This article focuses on the design of model predictive control (MPC) systems for nonlinear
processes that utilize an ensemble of recurrent neural network (RNN) models to predict …

Master Techniques and Successfully Build Models Using a Single Resource Vital to all data-
driven or measurement-based process operations, system identification is an interface that …

We consider the problem of learning a realization for a linear time-invariant (LTI) dynamical
system from input/output data. Given a single input/output trajectory, we provide finite time …

Operational modal analysis deals with the estimation of modal parameters from vibration
data obtained in operational rather than laboratory conditions. This paper extensively …

In this contribution we give an overview and discussion of the basic steps of System
Identification. The four main ingredients of the process that takes us from observed data to a …