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Artículos de revistas sobre el tema "Entropic optimal transport"

Autor: Grafiati

Publicado: 7 de diciembre de 2024

Última modificación: 31 de julio de 2025

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1

Altschuler, Jason M., Jonathan Niles-Weed, and Austin J. Stromme. "Asymptotics for Semidiscrete Entropic Optimal Transport." SIAM Journal on Mathematical Analysis 54, no. 2 (2022): 1718–41. http://dx.doi.org/10.1137/21m1440165.

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2

Keriven, Nicolas. "Entropic Optimal Transport on Random Graphs." SIAM Journal on Mathematics of Data Science 5, no. 4 (2023): 1028–50. http://dx.doi.org/10.1137/22m1518281.

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3

Amari, Shun-ichi, Ryo Karakida, Masafumi Oizumi, and Marco Cuturi. "Information Geometry for Regularized Optimal Transport and Barycenters of Patterns." Neural Computation 31, no. 5 (2019): 827–48. http://dx.doi.org/10.1162/neco_a_01178.

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We propose a new divergence on the manifold of probability distributions, building on the entropic regularization of optimal transportation problems. As Cuturi ( 2013 ) showed, regularizing the optimal transport problem with an entropic term is known to bring several computational benefits. However, because of that regularization, the resulting approximation of the optimal transport cost does not define a proper distance or divergence between probability distributions. We recently tried to introduce a family of divergences connecting the Wasserstein distance and the Kullback-Leibler divergence

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4

Rigollet, Philippe, and Jonathan Weed. "Entropic optimal transport is maximum-likelihood deconvolution." Comptes Rendus Mathematique 356, no. 11-12 (2018): 1228–35. http://dx.doi.org/10.1016/j.crma.2018.10.010.

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5

Clason, Christian, Dirk A. Lorenz, Hinrich Mahler, and Benedikt Wirth. "Entropic regularization of continuous optimal transport problems." Journal of Mathematical Analysis and Applications 494, no. 1 (2021): 124432. http://dx.doi.org/10.1016/j.jmaa.2020.124432.

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6

Junge, Oliver, Daniel Matthes, and Bernhard Schmitzer. "Entropic transfer operators." Nonlinearity 37, no. 6 (2024): 065004. http://dx.doi.org/10.1088/1361-6544/ad247a.

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Abstract We propose a new concept for the regularization and discretization of transfer and Koopman operators in dynamical systems. Our approach is based on the entropically regularized optimal transport between two probability measures. In particular, we use optimal transport plans in order to construct a finite-dimensional approximation of some transfer or Koopman operator which can be analyzed computationally. We prove that the spectrum of the discretized operator converges to the one of the regularized original operator, give a detailed analysis of the relation between the discretized and

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7

Amid, Ehsan, Frank Nielsen, Richard Nock, and Manfred K. Warmuth. "Optimal Transport with Tempered Exponential Measures." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 10838–46. http://dx.doi.org/10.1609/aaai.v38i10.28957.

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In the field of optimal transport, two prominent subfields face each other: (i) unregularized optimal transport, ``a-la-Kantorovich'', which leads to extremely sparse plans but with algorithms that scale poorly, and (ii) entropic-regularized optimal transport, ``a-la-Sinkhorn-Cuturi'', which gets near-linear approximation algorithms but leads to maximally un-sparse plans. In this paper, we show that an extension of the latter to tempered exponential measures, a generalization of exponential families with indirect measure normalization, gets to a very convenient middle ground, with both very fa

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8

PEYRÉ, GABRIEL, LÉNAÏC CHIZAT, FRANÇOIS-XAVIER VIALARD, and JUSTIN SOLOMON. "Quantum entropic regularization of matrix-valued optimal transport." European Journal of Applied Mathematics 30, no. 6 (2017): 1079–102. http://dx.doi.org/10.1017/s0956792517000274.

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This article introduces a new notion of optimal transport (OT) between tensor fields, which are measures whose values are positive semidefinite (PSD) matrices. This “quantum” formulation of optimal transport (Q-OT) corresponds to a relaxed version of the classical Kantorovich transport problem, where the fidelity between the input PSD-valued measures is captured using the geometry of the Von-Neumann quantum entropy. We propose a quantum-entropic regularization of the resulting convex optimization problem, which can be solved efficiently using an iterative scaling algorithm. This method is a ge

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9

Ito, Kaito, and Kenji Kashima. "Entropic model predictive optimal transport over dynamical systems." Automatica 152 (June 2023): 110980. http://dx.doi.org/10.1016/j.automatica.2023.110980.

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10

Lo, Chi-Jen (Roger), Mahesh K. Marina, Nishanth Sastry, Kai Xu, Saeed Fadaei, and Yong Li. "Shrinking VOD Traffic via Rényi-Entropic Optimal Transport." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 1 (2024): 1–34. http://dx.doi.org/10.1145/3639033.

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In response to the exponential surge in Internet Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in the sense of cost) to fulfil each individual user's preference. To quantify the cache footprint of VOD traffic, we pr

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11

Lo, Chi-Jen (Roger), Mahesh K. Marina, Nishanth Sastry, Kai Xu, Saeed Fadaei, and Yong Li. "Shrinking VOD Traffic via Rényi-Entropic Optimal Transport." ACM SIGMETRICS Performance Evaluation Review 52, no. 1 (2024): 75–76. http://dx.doi.org/10.1145/3673660.3655081.

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In response to the exponential surge in Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in terms of cost) to fulfil individual user's preference.

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12

Dupuy, Arnaud, Alfred Galichon, and Yifei Sun. "Estimating matching affinity matrices under low-rank constraints." Information and Inference: A Journal of the IMA 8, no. 4 (2019): 677–89. http://dx.doi.org/10.1093/imaiai/iaz015.

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Abstract In this paper, we address the problem of estimating transport surplus (a.k.a. matching affinity) in high-dimensional optimal transport problems. Classical optimal transport theory specifies the matching affinity and determines the optimal joint distribution. In contrast, we study the inverse problem of estimating matching affinity based on the observation of the joint distribution, using an entropic regularization of the problem. To accommodate high dimensionality of the data, we propose a novel method that incorporates a nuclear norm regularization that effectively enforces a rank co

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13

Tenetov, Evgeny, Gershon Wolansky, and Ron Kimmel. "Fast Entropic Regularized Optimal Transport Using Semidiscrete Cost Approximation." SIAM Journal on Scientific Computing 40, no. 5 (2018): A3400—A3422. http://dx.doi.org/10.1137/17m1162925.

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14

Wang, Shuchan, Photios A. Stavrou, and Mikael Skoglund. "Generalizations of Talagrand Inequality for Sinkhorn Distance Using Entropy Power Inequality." Entropy 24, no. 2 (2022): 306. http://dx.doi.org/10.3390/e24020306.

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The distance that compares the difference between two probability distributions plays a fundamental role in statistics and machine learning. Optimal transport (OT) theory provides a theoretical framework to study such distances. Recent advances in OT theory include a generalization of classical OT with an extra entropic constraint or regularization, called entropic OT. Despite its convenience in computation, entropic OT still lacks sufficient theoretical support. In this paper, we show that the quadratic cost in entropic OT can be upper-bounded using entropy power inequality (EPI)-type bounds.

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15

Benamou, Jean-David, Wilbert L. Ijzerman, and Giorgi Rukhaia. "An entropic optimal transport numerical approach to the reflector problem." Methods and Applications of Analysis 27, no. 4 (2020): 311–40. http://dx.doi.org/10.4310/maa.2020.v27.n4.a1.

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16

Carlier, Guillaume, Vincent Duval, Gabriel Peyré, and Bernhard Schmitzer. "Convergence of Entropic Schemes for Optimal Transport and Gradient Flows." SIAM Journal on Mathematical Analysis 49, no. 2 (2017): 1385–418. http://dx.doi.org/10.1137/15m1050264.

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17

Shi, Liangliang, Zhaoqi Shen, and Junchi Yan. "Double-Bounded Optimal Transport for Advanced Clustering and Classification." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (2024): 14982–90. http://dx.doi.org/10.1609/aaai.v38i13.29419.

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Optimal transport (OT) is attracting increasing attention in machine learning. It aims to transport a source distribution to a target one at minimal cost. In its vanilla form, the source and target distributions are predetermined, which contracts to the real-world case involving undetermined targets. In this paper, we propose Doubly Bounded Optimal Transport (DB-OT), which assumes that the target distribution is restricted within two boundaries instead of a fixed one, thus giving more freedom for the transport to find solutions. Based on the entropic regularization of DB-OT, three scaling-base

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18

Di Marino, Simone, and Lénaïc Chizat. "A tumor growth model of Hele-Shaw type as a gradient flow." ESAIM: Control, Optimisation and Calculus of Variations 26 (2020): 103. http://dx.doi.org/10.1051/cocv/2020019.

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In this paper, we characterize a degenerate PDE as the gradient flow in the space of nonnegative measures endowed with an optimal transport-growth metric. The PDE of concern, of Hele-Shaw type, was introduced by Perthame et. al. as a mechanical model for tumor growth and the metric was introduced recently in several articles as the analogue of the Wasserstein metric for nonnegative measures. We show existence of solutions using minimizing movements and show uniqueness of solutions on convex domains by proving the Evolutional Variational Inequality. Our analysis does not require any regularity

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19

Guex, Guillaume, Ilkka Kivimäki, and Marco Saerens. "Randomized optimal transport on a graph: framework and new distance measures." Network Science 7, no. 1 (2019): 88–122. http://dx.doi.org/10.1017/nws.2018.29.

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AbstractThe recently developed bag-of-paths (BoP) framework consists in setting a Gibbs–Boltzmann distribution on all feasible paths of a graph. This probability distribution favors short paths over long ones, with a free parameter (the temperatureT) controlling the entropic level of the distribution. This formalism enables the computation of new distances or dissimilarities, interpolating between the shortest-path and the resistance distance, which have been shown to perform well in clustering and classification tasks. In this work, the bag-of-paths formalism is extended by adding two indepen

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20

Huizing, Geert-Jan, Gabriel Peyré, and Laura Cantini. "Optimal transport improves cell–cell similarity inference in single-cell omics data." Bioinformatics 38, no. 8 (2022): 2169–77. http://dx.doi.org/10.1093/bioinformatics/btac084.

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Abstract Motivation High-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification and characterization of cellular heterogeneity are typically achieved through unsupervised clustering, which crucially relies on a similarity metric. Results We here propose the use of Optimal Transport (OT) as a cell–cell similarity metric for single-cell omics data. OT defines distances to compare high-dimensional data represented as probability distributions. To speed up com

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21

Damodaran, Bharath Bhushan, Rémi Flamary, Vivien Seguy, and Nicolas Courty. "An Entropic Optimal Transport loss for learning deep neural networks under label noise in remote sensing images." Computer Vision and Image Understanding 191 (February 2020): 102863. http://dx.doi.org/10.1016/j.cviu.2019.102863.

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22

Muratore-Ginanneschi, Paolo, and Luca Peliti. "Classical uncertainty relations and entropy production in non-equilibrium statistical mechanics." Journal of Statistical Mechanics: Theory and Experiment 2023, no. 8 (2023): 083202. http://dx.doi.org/10.1088/1742-5468/ace3b3.

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Abstract We analyze Fürth’s 1933 classical uncertainty relations in the modern language of stochastic differential equations. Our interest is motivated by their application to non-equilibrium classical statistical mechanics. We show that Fürth’s uncertainty relations are a property inherent in martingales within the framework of a diffusion process. This result implies a lower bound on the fluctuations in current velocities of entropic quantifiers associated with transitions in stochastic thermodynamics. In cases of particular interest, we recover a well-known inequality for optimal mass trans

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23

Vashisht, Sagar, Dibakar Rakshit, Satyam Panchal, Michael Fowler, and Roydon Fraser. "Quantifying the Effects of Temperature and Depth of Discharge on Li-Ion Battery Heat Generation: An Assessment of Resistance Models for Accurate Thermal Behavior Prediction." ECS Meeting Abstracts MA2023-02, no. 3 (2023): 445. http://dx.doi.org/10.1149/ma2023-023445mtgabs.

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Li-ion batteries (LiBs) are widely adopted in electric vehicles (EVs) owing to their superior properties, such as high energy density, low discharge rate, long lifespan, and lightweight construction. Since the battery pack is the sole energy source for an EV, its performance is critical for optimal vehicle operation. However, the battery's calendar life, cycle life, and overall performance are significantly affected by temperature variations. The Li-ion batteries used in EVs may encounter challenging working conditions, leading to thermal problems such as significant capacity and power loss. I

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24

Wu, Liming. "Entropical Optimal Transport, Schrödinger’s System and Algorithms." Acta Mathematica Scientia 41, no. 6 (2021): 2183–97. http://dx.doi.org/10.1007/s10473-021-0623-1.

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25

Bao, Han, and Shinsaku Sakaue. "Sparse Regularized Optimal Transport with Deformed q-Entropy." Entropy 24, no. 11 (2022): 1634. http://dx.doi.org/10.3390/e24111634.

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Optimal transport is a mathematical tool that has been a widely used to measure the distance between two probability distributions. To mitigate the cubic computational complexity of the vanilla formulation of the optimal transport problem, regularized optimal transport has received attention in recent years, which is a convex program to minimize the linear transport cost with an added convex regularizer. Sinkhorn optimal transport is the most prominent one regularized with negative Shannon entropy, leading to densely supported solutions, which are often undesirable in light of the interpretabi

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26

Bonafini, Mauro, and Bernhard Schmitzer. "Domain decomposition for entropy regularized optimal transport." Numerische Mathematik 149, no. 4 (2021): 819–70. http://dx.doi.org/10.1007/s00211-021-01245-0.

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AbstractWe study Benamou’s domain decomposition algorithm for optimal transport in the entropy regularized setting. The key observation is that the regularized variant converges to the globally optimal solution under very mild assumptions. We prove linear convergence of the algorithm with respect to the Kullback–Leibler divergence and illustrate the (potentially very slow) rates with numerical examples. On problems with sufficient geometric structure (such as Wasserstein distances between images) we expect much faster convergence. We then discuss important aspects of a computationally efficien

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27

Tong, Qijun, and Kei Kobayashi. "Entropy-Regularized Optimal Transport on Multivariate Normal and q-normal Distributions." Entropy 23, no. 3 (2021): 302. http://dx.doi.org/10.3390/e23030302.

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The distance and divergence of the probability measures play a central role in statistics, machine learning, and many other related fields. The Wasserstein distance has received much attention in recent years because of its distinctions from other distances or divergences. Although computing the Wasserstein distance is costly, entropy-regularized optimal transport was proposed to computationally efficiently approximate the Wasserstein distance. The purpose of this study is to understand the theoretical aspect of entropy-regularized optimal transport. In this paper, we focus on entropy-regulari

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28

Venerus, David C., David Nieto Simavilla, and Jay D. Schieber. "THERMAL TRANSPORT IN CROSS-LINKED ELASTOMERS SUBJECTED TO ELONGATIONAL DEFORMATIONS." Rubber Chemistry and Technology 92, no. 4 (2019): 639–52. http://dx.doi.org/10.5254/rct.19.80382.

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ABSTRACT Investigations on thermal transport in cross-linked elastomers subjected to elongational deformations are reviewed and discussed. The focus is on experimental research, in which the deformation-induced anisotropy of the thermal conductivity tensor in several common elastomeric materials is measured using novel optical techniques developed in our laboratory. These sensitive and noninvasive techniques allow for the reliable measurement of thermal conductivity (diffusivity) tensor components on samples in a deformed state. When combined with measurements of the stress in deformed samples

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29

Santambrogio, Filippo. "Dealing with moment measures via entropy and optimal transport." Journal of Functional Analysis 271, no. 2 (2016): 418–36. http://dx.doi.org/10.1016/j.jfa.2016.04.009.

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30

Gentil, Ivan, Christian Léonard, and Luigia Ripani. "About the analogy between optimal transport and minimal entropy." Annales de la faculté des sciences de Toulouse Mathématiques 26, no. 3 (2017): 569–600. http://dx.doi.org/10.5802/afst.1546.

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31

Li, Gen, Yanxi Chen, Yu Huang, Yuejie Chi, H. Vincent Poor, and Yuxin Chen. "Fast Computation of Optimal Transport via Entropy-Regularized Extragradient Methods." SIAM Journal on Optimization 35, no. 2 (2025): 1330–63. https://doi.org/10.1137/23m1581443.

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32

Dolbeault, Jean, та Xingyu Li. "φ-Entropies: convexity, coercivity and hypocoercivity for Fokker–Planck and kinetic Fokker–Planck equations". Mathematical Models and Methods in Applied Sciences 28, № 13 (2018): 2637–66. http://dx.doi.org/10.1142/s0218202518500574.

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This paper is devoted to [Formula: see text]-entropies applied to Fokker–Planck and kinetic Fokker–Planck equations in the whole space, with confinement. The so-called [Formula: see text]-entropies are Lyapunov functionals which typically interpolate between Gibbs entropies and [Formula: see text] estimates. We review some of their properties in the case of diffusion equations of Fokker–Planck type, give new and simplified proofs, and then adapt these methods to a kinetic Fokker–Planck equation acting on a phase space with positions and velocities. At kinetic level, since the diffusion only ac

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33

Mihelich, M., D. Faranda, B. Dubrulle, and D. Paillard. "Statistical optimization for passive scalar transport: maximum entropy production versus maximum Kolmogorov–Sinai entropy." Nonlinear Processes in Geophysics 22, no. 2 (2015): 187–96. http://dx.doi.org/10.5194/npg-22-187-2015.

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Abstract. We derive rigorous results on the link between the principle of maximum entropy production and the principle of maximum Kolmogorov–Sinai entropy for a Markov model of the passive scalar diffusion called the Zero Range Process. We show analytically that both the entropy production and the Kolmogorov–Sinai entropy, seen as functions of a parameter f connected to the jump probability, admit a unique maximum denoted fmaxEP and fmaxKS. The behaviour of these two maxima is explored as a function of the system disequilibrium and the system resolution N. The main result of this paper is that

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34

Mihelich, M., D. Faranda, B. Dubrulle, and D. Paillard. "Statistical optimization for passive scalar transport: maximum entropy production vs. maximum Kolmogorov–Sinay entropy." Nonlinear Processes in Geophysics Discussions 1, no. 2 (2014): 1691–713. http://dx.doi.org/10.5194/npgd-1-1691-2014.

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Abstract. We derive rigorous results on the link between the principle of maximum entropy production and the principle of maximum Kolmogorov–Sinai entropy using a Markov model of the passive scalar diffusion called the Zero Range Process. We show analytically that both the entropy production and the Kolmogorov–Sinai entropy seen as functions of f admit a unique maximum denoted fmaxEP and fmaxKS. The behavior of these two maxima is explored as a function of the system disequilibrium and the system resolution N. The main result of this article is that fmaxEP and fmaxKS have the same Taylor expan

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35

Islas, Carlos, Pablo Padilla, and Marco Antonio Prado. "Information Processing in the Brain as Optimal Entropy Transport: A Theoretical Approach." Entropy 22, no. 11 (2020): 1231. http://dx.doi.org/10.3390/e22111231.

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We consider brain activity from an information theoretic perspective. We analyze the information processing in the brain, considering the optimality of Shannon entropy transport using the Monge–Kantorovich framework. It is proposed that some of these processes satisfy an optimal transport of informational entropy condition. This optimality condition allows us to derive an equation of the Monge–Ampère type for the information flow that accounts for the branching structure of neurons via the linearization of this equation. Based on this fact, we discuss a version of Murray’s law in this context.

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36

Bazaluk, Oleg, Sergiy Kotenko, and Vitalii Nitsenko. "Entropy as an Objective Function of Optimization Multimodal Transportations." Entropy 23, no. 8 (2021): 946. http://dx.doi.org/10.3390/e23080946.

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This article considers the use of the entropy method in the optimization and forecasting of multimodal transport under conditions of risks that can be determined simultaneously by deterministic, stochastic and fuzzy quantities. This will allow to change the route of transportation in real time in an optimal way with an unacceptable increase in the risk at one of its next stages and predict the redistribution of the load of transport nodes. The aim of this study is to develop a mathematical model for the optimal choice of an alternative route, the best for one or more objective functions in rea

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37

Datta, Nilanjana, and Cambyse Rouzé. "Relating Relative Entropy, Optimal Transport and Fisher Information: A Quantum HWI Inequality." Annales Henri Poincaré 21, no. 7 (2020): 2115–50. http://dx.doi.org/10.1007/s00023-020-00891-8.

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38

Bhatia, Rajendra, Tanvi Jain, and Yongdo Lim. "Strong convexity of sandwiched entropies and related optimization problems." Reviews in Mathematical Physics 30, no. 09 (2018): 1850014. http://dx.doi.org/10.1142/s0129055x18500149.

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We present several theorems on strict and strong convexity, and higher order differential formulae for sandwiched quasi-relative entropy (a parametrized version of the classical fidelity). These are crucial for establishing global linear convergence of the gradient projection algorithm for optimization problems for these functions. The case of the classical fidelity is of special interest for the multimarginal optimal transport problem (the [Formula: see text]-coupling problem) for Gaussian measures.

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39

Liero, Matthias, Alexander Mielke, and Giuseppe Savaré. "Optimal Entropy-Transport problems and a new Hellinger–Kantorovich distance between positive measures." Inventiones mathematicae 211, no. 3 (2017): 969–1117. http://dx.doi.org/10.1007/s00222-017-0759-8.

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40

Bigot, Jérémie, Elsa Cazelles, and Nicolas Papadakis. "Central limit theorems for entropy-regularized optimal transport on finite spaces and statistical applications." Electronic Journal of Statistics 13, no. 2 (2019): 5120–50. http://dx.doi.org/10.1214/19-ejs1637.

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41

Li, Haifeng, Jun Liu, Li Cui, Haiyang Huang, and Xue-Cheng Tai. "Volume preserving image segmentation with entropy regularized optimal transport and its applications in deep learning." Journal of Visual Communication and Image Representation 71 (August 2020): 102845. http://dx.doi.org/10.1016/j.jvcir.2020.102845.

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42

Feng, Qi, and Wuchen Li. "Entropy Dissipation for Degenerate Stochastic Differential Equations via Sub-Riemannian Density Manifold." Entropy 25, no. 5 (2023): 786. http://dx.doi.org/10.3390/e25050786.

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We studied the dynamical behaviors of degenerate stochastic differential equations (SDEs). We selected an auxiliary Fisher information functional as the Lyapunov functional. Using generalized Fisher information, we conducted the Lyapunov exponential convergence analysis of degenerate SDEs. We derived the convergence rate condition by generalized Gamma calculus. Examples of the generalized Bochner’s formula are provided in the Heisenberg group, displacement group, and Martinet sub-Riemannian structure. We show that the generalized Bochner’s formula follows a generalized second-order calculus of

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43

Christen, Thomas, and Frank Kassubek. "Entropy production moment closures and effective transport coefficients." Journal of Physics D: Applied Physics 47, no. 36 (2014): 363001. http://dx.doi.org/10.1088/0022-3727/47/36/363001.

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44

Hobbs, Bruce E., and Alison Ord. "The mechanics of granitoid systems and maximum entropy production rates." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1910 (2010): 53–93. http://dx.doi.org/10.1098/rsta.2009.0202.

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A model for the formation of granitoid systems is developed involving melt production spatially below a rising isotherm that defines melt initiation. Production of the melt volumes necessary to form granitoid complexes within 10 4 –10 7 years demands control of the isotherm velocity by melt advection. This velocity is one control on the melt flux generated spatially just above the melt isotherm, which is the control valve for the behaviour of the complete granitoid system. Melt transport occurs in conduits initiated as sheets or tubes comprising melt inclusions arising from Gurson–Tvergaard co

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45

Le, Xuan Hoang Khoa, Hakan F. Oztop, Fatih Selimefendigil, and Mikhail A. Sheremet. "Entropy Analysis of the Thermal Convection of Nanosuspension within a Chamber with a Heat-Conducting Solid Fin." Entropy 24, no. 4 (2022): 523. http://dx.doi.org/10.3390/e24040523.

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Heat transport augmentation in closed chambers can be achieved using nanofluids and extended heat transfer surfaces. This research is devoted to the computational analysis of natural convection energy transport and entropy emission within a closed region, with isothermal vertical borders and a heat-conducting solid fin placed on the hot border. Horizontal walls were assumed to be adiabatic. Control relations written using non-primitive variables with experimentally based correlations for nanofluid properties were computed by the finite difference technique. The impacts of the fin size, fin pos

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46

Huebener, R. P., and H. C. Ri. "Vortex transport entropy in cuprate superconductors and Boltzmann constant." Physica C: Superconductivity and its Applications 591 (December 2021): 1353975. http://dx.doi.org/10.1016/j.physc.2021.1353975.

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47

Zheng, Yunpeng, Mingchu Zou, Wenyu Zhang, et al. "Electrical and thermal transport behaviours of high-entropy perovskite thermoelectric oxides." Journal of Advanced Ceramics 10, no. 2 (2021): 377–84. http://dx.doi.org/10.1007/s40145-021-0462-5.

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AbstractOxide-based ceramics could be promising thermoelectric materials because of their thermal and chemical stability at high temperature. However, their mediocre electrical conductivity or high thermal conductivity is still a challenge for the use in commercial devices. Here, we report significantly suppressed thermal conductivity in SrTiO3-based thermoelectric ceramics via high-entropy strategy for the first time, and optimized electrical conductivity by defect engineering. In high-entropy (Ca0.2Sr0.2Ba0.2Pb0.2La0.2)TiO3 bulks, the minimum thermal conductivity can be 1.17 W/(m·K) at 923 K

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48

Cheng, Jiaxin, Yue Wu, Ayush Jaiswal, Xu Zhang, Pradeep Natarajan, and Prem Natarajan. "User-Controllable Arbitrary Style Transfer via Entropy Regularization." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 433–41. http://dx.doi.org/10.1609/aaai.v37i1.25117.

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Ensuring the overall end-user experience is a challenging task in arbitrary style transfer (AST) due to the subjective nature of style transfer quality. A good practice is to provide users many instead of one AST result. However, existing approaches require to run multiple AST models or inference a diversified AST (DAST) solution multiple times, and thus they are either slow in speed or limited in diversity. In this paper, we propose a novel solution ensuring both efficiency and diversity for generating multiple user-controllable AST results by systematically modulating AST behavior at run-tim

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49

Brizzi, Camilla, Guillaume Carlier, and Luigi De Pascale. "Entropic Approximation of $$\infty $$-Optimal Transport Problems." Applied Mathematics & Optimization 90, no. 1 (2024). http://dx.doi.org/10.1007/s00245-024-10136-3.

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AbstractWe propose an entropic approximation approach for optimal transportation problems with a supremal cost. We establish $$\Gamma $$ Γ -convergence for suitably chosen parameters for the entropic penalization and that this procedure selects $$\infty $$ ∞ -cyclically monotone plans at the limit. We also present some numerical illustrations performed with Sinkhorn’s algorithm.

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50

Carlier, Guillaume, Lénaïc Chizat, and Maxime Laborde. "Displacement smoothness of entropic optimal transport." ESAIM: Control, Optimisation and Calculus of Variations, February 29, 2024. http://dx.doi.org/10.1051/cocv/2024013.

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The function that maps a family of probability measures to the solution of the dual entropic optimal transport problem is known as the Schr ̈odinger map. We prove that when the cost function is Ck+1 with k ∈ N∗ then this map is Lipschitz continuous from the L2-Wasserstein space to the space of Ck functions. Our result holds on compact domains and covers the multi-marginal case. We also include regularity results under negative Sobolev metrics weaker than Wasserstein under stronger smoothness assumptions on the cost. As applications, we prove displacement smoothness of the entropic optimal tran

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