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Optimal design of water distribution systems based on entropy and topology [PDF]

open access: yesWater Resources Management, 2014
A new multi-objective evolutionary optimization approach for joint topology and pipe size design of water distribution systems is presented. The algorithm proposed considers simultaneously the adequacy of flow and pressure at the demand nodes; the ...
Saleh, Salah H A, Tanyimboh, Tiku
core   +4 more sources

Topological entanglement entropy in d-dimensions for Abelian higher gauge theories [PDF]

open access: yesJournal of High Energy Physics, 2020
We compute the topological entanglement entropy for a large set of lattice models in d-dimensions. It is well known that many such quantum systems can be constructed out of lattice gauge models.
J.P. Ibieta-Jimenez   +3 more
doaj   +3 more sources

Entropy as a Topological Operad Derivation [PDF]

open access: yesEntropy, 2021
We share a small connection between information theory, algebra, and topology—namely, a correspondence between Shannon entropy and derivations of the operad of topological simplices.
Tai-Danae Bradley
doaj   +2 more sources

A Network Structure Entropy Considering Series-Parallel Structures

open access: yesEntropy, 2022
Entropy is an important indicator to measure network heterogeneity. We propose a new network structure entropy, SP (series-parallel) structure entropy, based on the global network topology while adding a medial measure that considers the series-parallel ...
Meng Cai, Jiaqi Liu, Ying Cui
doaj   +2 more sources

Topology and Phase Transitions: A First Analytical Step towards the Definition of Sufficient Conditions

open access: yesEntropy, 2021
Different arguments led to supposing that the deep origin of phase transitions has to be identified with suitable topological changes of potential related submanifolds of configuration space of a physical system.
Loris Di Cairano   +2 more
doaj   +2 more sources

Thermodynamic analysis of black holes with cloud of strings and quintessence via Barrow entropy

open access: yesPhysics Letters B
We explore a Reissner-Nordström Anti-de Sitter (RN-AdS) black hole with a cloud of string and quintessence to study thermodynamics and thermodynamic topology in the presence of Barrow entropy, which is currently being used widely as the horizon of a ...
Usman Zafar   +3 more
doaj   +2 more sources

Entanglement entropy and edge modes in topological string theory. Part I. Generalized entropy for closed strings [PDF]

open access: yesJournal of High Energy Physics, 2021
Abstract Progress in identifying the bulk microstate interpretation of the Ryu-Takayanagi formula requires understanding how to define entanglement entropy in the bulk closed string theory. Unfortunately, entanglement and Hilbert space factorization remains poorly understood in string theory.
William Donnelly   +3 more
openaire   +3 more sources

GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy [PDF]

open access: yesIEEE International Conference on Data Engineering, 2023
Graph neural networks (GNNs) have shown ad-vantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes have dissimilar features and ...
Tianhao Peng   +8 more
semanticscholar   +1 more source

$$C^0$$ C 0 -robustness of topological entropy for geodesic flows [PDF]

open access: yesJournal of Fixed Point Theory and Applications, 2021
In this paper, we study the regularity of topological entropy, as a function on the space of Riemannian metrics endowed with the $$C^0$$ C 0 topology. We establish several instances of entropy robustness (persistence of positive entropy after small $$C^0$
Marcelo R. R. Alves   +3 more
semanticscholar   +1 more source

Self-Supervised Graph Representation Learning via Topology Transformations [PDF]

open access: yesIEEE Transactions on Knowledge and Data Engineering, 2021
We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data to enable the wide applicability of Graph Convolutional Neural Networks (GCNNs).
Xiang Gao, Wei Hu, Guo-Jun Qi
semanticscholar   +1 more source

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