We propose a methodology for the identification of nonlinear state–space models from
input/output data using machine-learning techniques based on autoencoders and neural …

Koopman operator theory enables a global linear representation of a given nonlinear
dynamical system. However, since an approximation to the Koopman operator cannot fully …

I have been thinking a lot about how machine learning (ML) and related areas (eg, artificial
intelligence, digitalization, and data science) are transforming and will transform our …

Industrial production of valuable chemical products often involves the manipulation of
phenomena evolving at different temporal and spatial scales. Product properties can be …

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 …

Process systems engineering research often utilizes virtual testbeds consisting of
physicsbased process models. As machine learning and image processing become more …

We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in
manufacturing. We begin by discussing how different types of data objects commonly …

There are many methods for real-time estimation and identification for induction motor (IM)
drives. In this study, dynamic mode decomposition with control (DMDc) and its variants are …

We consider a dynamic mode decomposition (DMD) based technique to identify data-driven
reduced-order and full-order models and propose two approaches to update them in real …

As a typical nonlinear system, the prediction of state variables and system control in
distillation column systems face numerous challenges. The dynamic mode decomposition …