Local Geometric and Transport Properties of Networks that are Generated from Hyperuniform Point Patterns
University of California, Los Angeles（加州大学洛杉矶分校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Tandon School of Engineering, New York University（纽约大学坦登工程学院）；Santa Fe Institute（圣塔菲研究所）；Nordita（北欧理论天体物理学研究所）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Stockholm University（斯德哥尔摩大学）；Uppsala University（乌普萨拉大学）
James V. Raj, Xiaohan Sun, Charles Emmett Maher, Katherine A. Newhall, Mason A. Porter
备注：
significantly revised and expanded from previous version; 18 pages; 10 figures, still lots of ORCs; the abstract on the arXiv page is significantly abridged due to the maximum length requirement
Hyperuniformity, which is a type of order that is characterized by the suppression of long-range density fluctuations in comparison to the fluctuations in standard disordered systems, has emerged as a powerful concept in many natural and engineered phenomena. In this paper, we harness hyperuniform point patterns to generate disordered, spatially-embedded networks that are distinct from both perfectly ordered lattices and uniformly random spatial networks. We refer to this class of disordered networks as \emph{hyperuniform-point-pattern-induced (HuPPI) networks}, and we compare them to counterpart \emph{Poisson-point-pattern-induced (PoPPI) networks} and networks that we generate from random-sequential-adsorption (RSA) packings (which have local order but are not hyperuniform). We examine HuPPI, PoPPI, and RSA networks that we generate using Gabriel, Delaunay, Delaunay-centroidal, and Voronoi network-generation methods. We show that HuPPI networks have smaller mean normalized total effective resistances (TERs) than PoPPI networks in Gabriel, Delaunay-centroidal, and Voronoi networks and have shorter mean random-walk mixing times than PoPPI networks in Gabriel, Delaunay, and Delaunay-centroidal networks. For all four network-generation methods and for both HuPPI and PoPPI networks, we find that the Ollivier--Ricci curvature (ORC) of an edge is negatively correlated with its TER edge contribution (i.e., the increase in TER that results from removing that edge). Collectively, our results (1) illustrate how the local network geometry that ORC measures is related to transport and network connectivity and (2) demonstrate that the network-generation method provides an additional design lever for spatial networks beyond the choice of their underlying point pattern. [This abstract is abridged significantly from the full abstract.]
Persistence of asymptotic variance under transport: from hyperfluctuation to stealthy hyperuniformity
German Aerospace Center (DLR)（德国航空航天中心）；Institute for Stochastics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院概率论研究所）；Institute for AI Safety and Security（人工智能安全与保护研究所）；Department of Physics, Ludwig-Maximilians-Universität München（慕尼黑大学物理系）
Inferring stealthy hyperuniform correlations from quantum transport
Instituto de Física, Universidade Federal do Rio de Janeiro（里约热内卢联邦大学物理研究所）；Institut für Theoretische Physik und Astrophysik, Universität Würzburg（维尔茨堡大学理论物理与天体物理研究所）；School of Physics, Trinity College Dublin（都柏林圣三一学院物理学院）；Centre for Research on Adaptive Nanostructures and Nanodevices (CRANN) & Advanced Materials and Bioengineering Research (AMBER) Centre, Trinity College Dublin（都柏林圣三一学院自适应纳米结构与纳米器件研究中心及先进材料与生物工程技术研究中心）；Department of Physics, Princeton University（普林斯顿大学物理系）；Department of Chemistry, Princeton University（普林斯顿大学化学系）；Princeton Institute for the Science and Technology of Materials, Princeton University（普林斯顿大学材料科学与工程研究所）；Program in Applied and Computational Mathematics, Princeton University（普林斯顿大学应用与计算数学项目）
Predictive Formulas for Scattering Mean Free Path for General Disordered Dielectric Media Beyond the Long-Wavelength Regime
Princeton Materials Institute, Princeton University（普林斯顿大学材料研究所）；Department of Chemistry, Princeton University（普林斯顿大学化学系）；Department of Physics, Princeton University（普林斯顿大学物理系）；Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院化学与生物分子工程系）；GIST InnoCORE AI-Nano Convergence Institute for Early Detection of Neurodegenerative Diseases, Gwangju Institute of Science and Technology（光州科学技术研究院GIST InnoCORE神经退行性疾病早期检测AI-纳米融合研究所）；Program in Applied and Computational Mathematics, Princeton University（普林斯顿大学应用与计算数学项目）
Identifying geometric third-order nonlinear transport in disordered materials
Southern University of Science and Technology (SUSTech)（南方科技大学）；Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area (Guangdong)（粤港澳大湾区量子科学中心）；Peking University（北京大学）
