Lectures related to Logical matrix

Covers calibrating models using measurements, virtual sensors, data reconciliation, and parameter identification in process models.

Explores consensus in networked control systems through graph weight design and matrix properties.

Explores process unit modeling, balance equations, degrees of freedom, and flowsheet solving.

Explores the Ford Fulkerson algorithm, Max-Flow Min-Cut theorem, Incidence matrix, and network optimization complexity.

Explores properties of matrices, irreducibility, and graph connectivity in networked control systems.

Covers the transhipment problem, optimal solutions, and total unimodularity in matrices.

Covers the linear optimization problem of the transhipment model.

Explores the consensus theorem for communication networks and the implications of various consensus properties in networked control systems.

Explores consensus algorithms in digraphs, focusing on scenarios where the digraph is not strongly connected.

Explores the connection between graphs and probabilities, emphasizing modular and super modular probabilities and correlation properties.

Explores graphs and matrices, including adjacency, degree, and Laplace matrices, Matrix-tree theorem, and spanning trees.

Explores link prediction in networks, covering missing edges, probabilistic methods, and causal inference challenges.

Introduces graph theory, network flows, and flow conservation laws with practical examples and theorems.

Covers the spatial response of systems, including the calculation of the center of mass and convolution.

Covers the transhipment problem, the incidence matrix, and total unimodularity.

Explores the role of graphs in deep learning, focusing on their structure, applications, and techniques for processing graph data.

Covers graphical models for probability distributions and factor graphs representation.