Today's manufacturing processes are pushed to their limits to generate products with ever-
increasing quality at low costs. A prominent hurdle on this path arises from the multiscale …

Automated data-driven modeling, the process of directly discovering the governing
equations of a system from data, is increasingly being used across the scientific community …

This work develops a physics-informed transfer learning framework for modeling and control
of a nonlinear process network with limited training data. Unlike the conventional transfer …

We propose a framework and an algorithm to uncover the unknown parts of nonlinear
equations directly from data. The framework is based on eXtended Physics-Informed Neural …

In this paper, we propose an efficient data-driven predictive control approach for general
nonlinear processes based on a reduced-order Koopman operator. A Kalman-based sparse …

Pattern-forming systems can exhibit a diverse array of complex behaviors as external
parameters are varied, enabling a variety of useful functions in biological and engineered …

In this paper, we present a data-driven regression approach to identify parametric governing
equations from time-series data. Iterative computations are performed for each time stamp to …

A complex physics-based modeling procedure and the uncertainty and confidentiality of
internal parameters of distributed energy resources (DERs) motivate system identification …

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots,
pose a challenging control problem due to their high dimensionality and heavy non …

Modularized sparse identification (M-SINDy) is developed in this paper for effective data-
driven modeling of the nonlinear transient dynamics of microgrid systems. The high …