This book presents an overview of the different errors-in-variables (EIV) methods that can be
used for system identification. Readers will explore the properties of an EIV problem. Such …

Network systems have become a ubiquitous modeling tool in many areas of science where
nodes in a graph represent distributed processes and edges between nodes represent a …

This paper presents a “structured” learning approach for the identification of continuous
partial differential equation (PDE) models with both constant and spatial-varying coefficients …

Reconstructing the causal structure of a network of dynamic systems from observational data
is an important problem in many areas of science. One of the earliest and most prominent …

This paper considers the problem of learning the unknown structure of a network with the
underlying topology given by a polyforest (a collection of directed trees with potentially …

Performing multiple experiments is common when learning internal mechanisms of complex
systems. These experiments can include perturbations of parameters or external …

The paper gives a survey of errors-in-variables methods in system identification.
Background and motivation are given, and examples illustrate why the identification problem …

Identifying influential nodes in network dynamical systems requires the manipulation of
topological and dynamic characteristics within ideal experiments. However, seldom we have …

We propose a method that simultaneously identifies a sparse transfer matrix and a
disturbance signal for a multizone building's temperature dynamics from the measurements …

We introduce a System Evolution Recommender (SysEvoRecomd) algorithm that uses a
novel algorithm Graph Evolution and Change Learning (GECL) to do system network …