In the literature, machine learning (ML) and artificial intelligence (AI) applications tend to
start with examples that are irrelevant to process engineers (eg classification of images …

Part 2 of this monograph builds on the introduction to tensor networks and their operations
presented in Part 1. It focuses on tensor network models for super-compressed higher-order …

We present a physics‐informed deep neural network (DNN) method for estimating hydraulic
conductivity in saturated and unsaturated flows governed by Darcy's law. For saturated flow …

In this work we present a data-driven method for the discovery of parametric partial
differential equations (PDEs), thus allowing one to disambiguate between the underlying …

During the past half century, modern control theory has developed greatly and many
branches and subfields have emerged, for example, linear system theory, optimal control …

A critical challenge in the data-driven modeling of dynamical systems is producing methods
robust to measurement error, particularly when data is limited. Many leading methods either …

This chapter addresses many fundamental issues arising when transitioning from nonlinear
static to nonlinear dynamic models. Many aspects are very general in nature and …

Identification algorithms based on the well-known linear least squares methods of gaussian
elimination, Cholesky decomposition, classical Gram-Schmidt, modified Gram-Schmidt …

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Many types of engineering structures exhibit nonlinear behavior under real operating
conditions. Sometimes the unpredicted nonlinear behavior of a system results in …