Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9242
Main Title: Maximum Entropy Analysis of Flow Networks: Theoretical Foundation and Applications
Author(s): Niven, Robert K.
Abel, Markus
Schlegel, Michael
Waldrip, Steven H.
Type: Article
Language Code: en
Abstract: The concept of a “flow network”—a set of nodes and links which carries one or more flows—unites many different disciplines, including pipe flow, fluid flow, electrical, chemical reaction, ecological, epidemiological, neurological, communications, transportation, financial, economic and human social networks. This Feature Paper presents a generalized maximum entropy framework to infer the state of a flow network, including its flow rates and other properties, in probabilistic form. In this method, the network uncertainty is represented by a joint probability function over its unknowns, subject to all that is known. This gives a relative entropy function which is maximized, subject to the constraints, to determine the most probable or most representative state of the network. The constraints can include “observable” constraints on various parameters, “physical” constraints such as conservation laws and frictional properties, and “graphical” constraints arising from uncertainty in the network structure itself. Since the method is probabilistic, it enables the prediction of network properties when there is insufficient information to obtain a deterministic solution. The derived framework can incorporate nonlinear constraints or nonlinear interdependencies between variables, at the cost of requiring numerical solution. The theoretical foundations of the method are first presented, followed by its application to a variety of flow networks.
URI: https://depositonce.tu-berlin.de/handle/11303/10280
http://dx.doi.org/10.14279/depositonce-9242
Issue Date: 8-Aug-2019
Date Available: 8-Nov-2019
DDC Class: 510 Mathematik
Subject(s): maximum entropy analysis
flow network
probabilistic inference
License: https://creativecommons.org/licenses/by/4.0/
Journal Title: Entropy
Publisher: MDPI
Publisher Place: Basel
Volume: 21
Issue: 8
Article Number: 776
Publisher DOI: 10.3390/e21080776
EISSN: 1099-4300
Appears in Collections:FG Numerische Fluiddynamik » Publications

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