Tootfinder

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

Flow-Attentional Graph Neural Networks
Pascal Plettenberg, Dominik K\"ohler, Bernhard Sick, Josephine M. Thomas
https://arxiv.org/abs/2506.06127 https…

Flow-Attentional Graph Neural Networks
Graph Neural Networks (GNNs) have become essential for learning from graph-structured data. However, existing GNNs do not consider the conservation law inherent in graphs associated with a flow of physical resources, such as electrical current in power grids or traffic in transportation networks, which can lead to reduced model performance. To address this, we propose flow attention, which adapts existing graph attention mechanisms to satisfy Kirchhoffś first law. Furthermore, we discuss how t…

Dynamic link switching induces stable synchronized states in sparse networks
Muhittin Cenk Eser, Everton S. Medeiros, Mustafa Riza, Maximilian Engel
https://arxiv.org/abs/2507.08007

Dynamic link switching induces stable synchronized states in sparse networks
The flow of information in networked systems composed of multiple interacting elements strongly depends on the level of connectivity among these elements. Sparse connectivity often hinders the emergence of states where information is globally shared, such as fully synchronized states. In this context, dynamically switching the existing network links among the system elements can facilitate the onset of synchronization. However, determining the optimal rate at which links should be switched to e…

Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks
Laksh Patel, Neel Shanbhag
https://arxiv.org/abs/2506.08024 https://

Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks
Distributed supply-chain optimization demands algorithms that can cope with unreliable communication, unbounded messaging delays, and geographically dispersed agents while still guaranteeing convergence with provable rates. In this work, we introduce DAPD-SCO (Distributed Asynchronous Primal-Dual Optimization for Supply-Chain Networks), a fully asynchronous primal-dual scheme for network flow allocation over directed acyclic supply-chain graphs. Each edge agent independently updates its local f…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
Luis Kin Miyatake, Eduardo Camponogara, Eric Aislan Antonelo, Alexey Pavlov
https://arxiv.org/abs/2506.06188

Physics-Informed Neural Networks for Control of Single-Phase Flow Systems Governed by Partial Differential Equations
The modeling and control of single-phase flow systems governed by Partial Differential Equations (PDEs) present challenges, especially under transient conditions. In this work, we extend the Physics-Informed Neural Nets for Control (PINC) framework, originally proposed to modeling and control of Ordinary Differential Equations (ODE) without the need of any labeled data, to the PDE case, particularly to single-phase incompressible and compressible flows, integrating neural networks with physical…

Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates
Nis-Luca van H\"ulst, Pia Siegl, Paul Over, Sergio Bengoechea, Tomohiro Hashizume, Mario Guillaume Cecile, Thomas Rung, Dieter Jaksch
https://arxiv.org/abs/2507.05222

Quantum-Inspired Tensor-Network Fractional-Step Method for Incompressible Flow in Curvilinear Coordinates
We introduce an algorithmic framework based on tensor networks for computing fluid flows around immersed objects in curvilinear coordinates. We show that the tensor network simulations can be carried out solely using highly compressed tensor representations of the flow fields and the differential operators and discuss the numerical implementation of the tensor operations required for computing fluid flows in detail. The applicability of our method is demonstrated by applying it to the paradigm …

at_migrations: Austrian internal migrations (2002-2022)
A network of migrations between municipalities in Austria, from 2002 to 2022. A weighted directed link from source to target indicates a migration flow from these two municipalities. Edges are annotated with migration volume (number of people), nationality, sex, and year.
This network has 2115 nodes and 2908569 edges.
Tags: Social, Economic, Travel, Weighted, Politlcal, Timestamps, Metadata

Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks
Shrenik Jadhav, Birva Sevak, Srijita Das, Wencong Su, Van-Hai Bui
https://arxiv.org/abs/2507.02078 …

Enhancing Power Flow Estimation with Topology-Aware Gated Graph Neural Networks
Accurate and scalable surrogate models for AC power flow are essential for real-time grid monitoring, contingency analysis, and decision support in increasingly dynamic and inverter-dominated power systems. However, most existing surrogates fall short of practical deployment due to their limited capacity to capture long-range nonlinear dependencies in meshed transmission networks and their weak enforcement of physical laws. These models often require extensive hyperparameter tuning, exhibit poo…

Influential scientists shape knowledge flows between science and IGO policy
Kimitaka Asatani, Yurie Iwata, Yuta Tomokiyo, Basil Mahfouz, Masaru Yarime, Ichiro Sakata
https://arxiv.org/abs/2506.06753

Influential scientists shape knowledge flows between science and IGO policy
Intergovernmental organizations (IGOs) increasingly rely on scientific evidence, yet the pathways through which scientific research enters policy remain opaque. By linking 230,737 scientific papers cited in IGO policy documents (2015-2023) to their authors and collaboration networks, we identify a small group of policy-influential scientists (PI-Sci) who dominate this knowledge flow. These scientists form tightly interconnected, internationally spanning co-authorship networks and achieve policy…

Controlling Casimir Effect in Networks
Tian-Ming Zhao, Rong-Xin Miao
https://arxiv.org/abs/2506.20405 https://arxiv.org/pdf/2506.2040…

Controlling Casimir Effect in Networks
Networks have played a pivotal role in physics, mathematics, and biology, propelling the recent revolution in artificial intelligence. This paper studies quantum field theories on networks and proposes a novel junction condition on the node. The junction condition is consistent with energy conservation in the sense that the total energy flow into the node is zero. As an application, we explore the Casimir effect on networks. Remarkably, the Casimir force on one edge can be changed from attracti…

Canonical partial ordering from min-cuts and quantum entanglement in random tensor networks
Miao Hu, Ion Nechita
https://arxiv.org/abs/2506.23894 https://

Canonical partial ordering from min-cuts and quantum entanglement in random tensor networks
The \emph{max-flow min-cut theorem} has been recently used in the theory of random tensor networks in quantum information theory, where it is helpful for computing the behavior of important physical quantities, such as the entanglement entropy. In this paper, we extend the max-flow min-cut theorem to a relation among different \emph{partial orders} on the set of vertices of a network and introduce a new partial order for the vertices based on the \emph{min-cut structure} of the network. We appl…

Optimization of Bottlenecks in Quantum Graphs Guided by Fiedler Vector-Based Spectral Derivatives
John TM Campbell, John Dooley
https://arxiv.org/abs/2506.07875

Optimization of Bottlenecks in Quantum Graphs Guided by Fiedler Vector-Based Spectral Derivatives
This paper discusses the relationships between the Fiedler vector, the Cheeger constant, and threshold behaviors in networks of quantum resource nodes represented as Quantum Directed Acyclic Graphs (QDAGs). We explore how these mathematical constructs can be applied to understand the dynamics of quantum information flow in QDAGs, especially in the context of routing problems with bottlenecks in graph signal processing, and how new eigenvalue-based rewiring techniques can optimize entanglement d…

Transformative or Conservative? Conservation laws for ResNets and Transformers
Sibylle Marcotte, R\'emi Gribonval, Gabriel Peyr\'e
https://arxiv.org/abs/2506.06194

Transformative or Conservative? Conservation laws for ResNets and Transformers
While conservation laws in gradient flow training dynamics are well understood for (mostly shallow) ReLU and linear networks, their study remains largely unexplored for more practical architectures. This paper bridges this gap by deriving and analyzing conservation laws for modern architectures, with a focus on convolutional ResNets and Transformer networks. For this, we first show that basic building blocks such as ReLU (or linear) shallow networks, with or without convolution, have easily exp…

S\"{o}ze: One Network Telemetry Is All You Need for Per-flow Weighted Bandwidth Allocation at Scale
Weitao Wang, T. S. Eugene Ng
https://arxiv.org/abs/2506.00834

Söze: One Network Telemetry Is All You Need for Per-flow Weighted Bandwidth Allocation at Scale
Weighted bandwidth allocation is a powerful abstraction that has a wide range of use cases in modern data center networks. However, realizing highly agile and precise weighted bandwidth allocation for large-scale cloud environments is fundamentally challenging. In this paper, we propose Söze, a lightweight decentralized weighted bandwidth allocation system that leverages simple network telemetry features of commodity Ethernet switches. Given the flow weights, Söze can effectively use the tele…

Breadth, Depth, and Flux of Course-Prerequisite Networks
Konstantin Zuev, Pavlos Stavrinides
https://arxiv.org/abs/2506.23510 https://

Breadth, Depth, and Flux of Course-Prerequisite Networks
Course-prerequisite networks (CPNs) are directed acyclic graphs that model complex academic curricula by representing courses as nodes and dependencies between them as directed links. These networks are indispensable tools for visualizing, studying, and understanding curricula. For example, CPNs can be used to detect important courses, improve advising, guide curriculum design, analyze graduation time distributions, and quantify the strength of knowledge flow between different university depart…

This https://arxiv.org/abs/2404.08210 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The Inverse Carson's Equations Problem: Definition, Implementation and Numerical Experiments
In recent years, with the increase in renewable energy and storage penetration, power flow studies in low-voltage networks have become of interest in both industry and academia. Many studies use impedance represented by sequence components due to the lack of datasets with fully parameterized impedance matrices. This assumes that the network impedance is balanced, which is typically not the case in the low-voltage network and therefore risks the accuracy of the study. This paper proposes a metho…

This https://arxiv.org/abs/2505.24434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields
Flow matching casts sample generation as learning a continuous-time velocity field that transports noise to data. Existing flow matching networks typically predict each point's velocity independently, considering only its location and time along its flow trajectory, and ignoring neighboring points. However, this pointwise approach may overlook correlations between points along the generation trajectory that could enhance velocity predictions, thereby improving downstream generation quality. To …

Predicting Flow-Induced Vibration in Isolated and Tandem Cylinders Using Hypergraph Neural Networks
Shayan Heydari, Rui Gao, Rajeev K Jaiman
https://arxiv.org/abs/2507.02242

Predicting Flow-Induced Vibration in Isolated and Tandem Cylinders Using Hypergraph Neural Networks
We present a finite element-inspired hypergraph neural network framework for predicting flow-induced vibrations in freely oscillating cylinders. The surrogate architecture transforms unstructured computational meshes into node-element hypergraphs that encode higher-order spatial relationships through element-based connectivity, preserving the geometric and topological structure of the underlying finite-element discretization. The temporal evolution of the fluid-structure interaction is modeled …

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Yejing Wang, Shengyu Zhou, Jinyu Lu, Qidong Liu, Xinhang Li, Wenlin Zhang, Feng Li, Pengjie Wang, Jian Xu, Bo Zheng, Xiangyu Zhao
https://arxiv.org/abs/2506.16114

GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios. The majority of existing research efforts primarily concentrate on developing powerful item tokenizers or advancing LLM decoding strategies to attain superior performance. However, the critical fine-tuning step in GR frameworks, which is essential for adapting LLMs to recommendation data, remains largely unexpl…

This https://arxiv.org/abs/2411.13280 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks
Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO),…

Centre driven Controlled Evolution of Wireless Virtual Networks based on Broadcast Tokens
Vignesh Babu, Atishay Jain, Kannan Karthik
https://arxiv.org/abs/2506.16615

Centre driven Controlled Evolution of Wireless Virtual Networks based on Broadcast Tokens
In a wireless sensor network, the virtual connectivity between nodes is a function of the keys shared between various nodes. Pre-embedding these key configurations in the nodes would make the network inflexible. On the other hand, permitting subsets of nodes to engage in a common key synthesis phase to create secure distributed connections amongst themselves, would decouple and conceal the information flow from the controlling centre. An intermediate solution is the notion of a centre driven ke…

Replaced article(s) found for physics.data-an. https://arxiv.org/list/physics.data-an/new
[1/1]:
- Self-consistent gravity model for inferring node mass in flow networks
Daekyung Lee, Wonguk Cho, Heetae Kim, Gunn Kim, Hyeong-Chai Jeong, Beom Jun Kim

Preserving spreading dynamics and information flow in complex network reduction
Dan Chen, Housheng Su, Yong Wang, Jie Liu
https://arxiv.org/abs/2506.18641 …

Preserving spreading dynamics and information flow in complex network reduction
Effectively preserving both the structural and dynamical properties during the reduction of complex networks remains a significant research topic. Existing network reduction methods based on renormalization group or sampling often face challenges such as high computational complexity and the loss of critical dynamic attributes. This paper proposes an efficient network reduction framework based on subgraph extraction, which accurately preserves epidemic spreading dynamics and information flow th…

Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks
Salah Eddine Choutri, Boualem Djehiche, Prajwal Chauhan, Saif Eddin Jabari
https://arxiv.org/abs/2506.03059

Backpressure-based Mean-field Type Game for Scheduling in Multi-Hop Wireless Sensor Networks
We propose a Mean-Field Type Game (MFTG) framework for effective scheduling in multi-hop wireless sensor networks (WSNs) using backpressure as a performance criterion. Traditional backpressure algorithms leverage queue differentials to regulate data flow and maintain network stability. In this work, we extend the backpressure framework by incorporating a mean-field term into the cost functional, capturing the global behavior of the system alongside local dynamics. The resulting model utilizes t…

Polymer-modulated evaporation flow enables scalable self-assembly of highly aligned nanowires
Liyiming Tao, Zechao Jiang, Shiyuan Hu, Lin Du, Qiuting Zhang, Jiajia Zhou, Masao Doi, Xiaojun Wu, Xingkun Man, Ye Xu
https://arxiv.org/abs/2505.23227

Polymer-modulated evaporation flow enables scalable self-assembly of highly aligned nanowires
Highly aligned nanowire networks are essential for enabling anisotropic optical, electrical, and sensing functionalities in next-generation devices. However, achieving such alignment typically requires complex fabrication methods or high-energy processing. Here, we present a simple and scalable self-assembly strategy that uses a viscosity-enhancing polymer additive to modulate fluid flows during solvent evaporation. The addition of carboxymethylcellulose sodium (CMC-Na) reshapes the evaporation…

Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
Zhihao Tao, Athina P. Petropulu
https://arxiv.org/abs/2506.14992 https://…

Secure Time-Modulated Intelligent Reflecting Surface via Generative Flow Networks
We propose a novel directional modulation (DM) design for OFDM transmitters aided by a time-modulated intelligent reflecting surface (TM-IRS). The TM-IRS is configured to preserve the integrity of transmitted signals toward multiple legitimate users while scrambling the signal in all other directions. Existing TM-IRS design methods typically target a single user direction and follow predefined rule-based procedures, making them unsuitable for multi-user scenarios. Here, we propose a generative …

This https://arxiv.org/abs/2506.00834 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNI_…

Söze: One Network Telemetry Is All You Need for Per-flow Weighted Bandwidth Allocation at Scale
Weighted bandwidth allocation is a powerful abstraction that has a wide range of use cases in modern data center networks. However, realizing highly agile and precise weighted bandwidth allocation for large-scale cloud environments is fundamentally challenging. In this paper, we propose Söze, a lightweight decentralized weighted bandwidth allocation system that leverages simple network telemetry features of commodity Ethernet switches. Given the flow weights, Söze can effectively use the tele…

Circular Directional Flow Decomposition of Networks
Marc Homs-Dones, Robert S. MacKay, Bazil Sansom, Yijie Zhou
https://arxiv.org/abs/2506.12546 https://…

Circular Directional Flow Decomposition of Networks
We introduce the Circular Directional Flow Decomposition (CDFD), a new framework for analyzing circularity in weighted directed networks. CDFD separates flow into two components: a circular (divergence-free) component and an acyclic component that carries all nett directional flow. This yields a normalized circularity index between 0 (fully acyclic) and 1 (for networks formed solely by the superposition of cycles), with the complement measuring directionality. This index captures the proportion…

Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
Godwin Badu-Marfo, Bilal Farooq
https://arxiv.org/abs/2507.05507

Dynamic Campus Origin-Destination Mobility Prediction using Graph Convolutional Neural Network on WiFi Logs
We present an integrated graph-based neural networks architecture for predicting campus buildings occupancy and inter-buildings movement at dynamic temporal resolution that learns traffic flow patterns from Wi-Fi logs combined with the usage schedules within the buildings. The relative traffic flows are directly estimated from the WiFi data without assuming the occupant behaviour or preferences while maintaining individual privacy. We formulate the problem as a data-driven graph structure repre…

On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach
Zien Wang, Xiucheng Wang, Nan Cheng, Wenchao Xu, Wei Quan, Ruijin Sun, Conghao Zhou
https://arxiv.org/abs/2507.04589

On-Demand Multimedia Delivery in 6G: An Optimal-Cost Steiner Tree Approach
The exponential growth of multimedia data traffic in 6G networks poses unprecedented challenges for immersive communication, where ultra-high-definition, multi-quality streaming must be delivered on demand while minimizing network operational costs. Traditional routing approaches, such as shortest-path algorithms, fail to optimize flow multiplexing across multiple destinations, while conventional Steiner tree methods cannot accommodate heterogeneous quality-of-service (QoS) requirements-a criti…

This https://arxiv.org/abs/2304.05775 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Mapping compact memory-biased dynamics reveals overlapping communities
Researchers use networks to model relational data from complex systems and apply tools from network science to reveal their organisation and function. Many real-world systems, from social networks to protein-protein interactions and species distributions, exhibit overlapping flow-based communities that reflect their functional organisation. However, identifying such overlapping flow-based communities typically requires higher-order relational data, which is often unavailable. To address this ch…

Transport-Generated Signals Uncover Geometric Features of Evolving Branched Structures
Fabian H. Kreten, Ludger Santen, Reza Shaebani
https://arxiv.org/abs/2506.11575

Transport-Generated Signals Uncover Geometric Features of Evolving Branched Structures
Branched structures that evolve over time critically determine the function of various natural and engineered systems, including growing vasculature, neural arborization, pulmonary networks such as lungs, river basins, power distribution networks, and synthetic flow media. Inferring the underlying geometric properties of such systems and monitoring their structural and morphological evolution is therefore essential. However, this remains a major challenge due to limited access and the transient…

IDGraphs: Intrusion Detection and Analysis Using Stream Compositing
Pin Ren, Yan Gao, Zhichun Li, Yan Chen, Benjamin Watson
https://arxiv.org/abs/2506.21425

IDGraphs: Intrusion Detection and Analysis Using Stream Compositing
Traffic anomalies and attacks are commonplace in today's networks and identifying them rapidly and accurately is critical for large network operators. For a statistical intrusion detection system (IDS), it is crucial to detect at the flow-level for accurate detection and mitigation. However, existing IDS systems offer only limited support for 1) interactively examining detected intrusions and anomalies, 2) analyzing worm propagation patterns, 3) and discovering correlated attacks. These problem…

Physics-Informed Neural Networks for Nonlocal Flow Modeling of Connected Automated Vehicles
Chenguang Zhao, Huan Yu
https://arxiv.org/abs/2506.18357 https:…

Physics-Informed Neural Networks for Nonlocal Flow Modeling of Connected Automated Vehicles
Connected automated vehicles (CAVs) cruising control strategies have been extensively studied at the microscopic level. CAV controllers sense and react to traffic both upstream and downstream, yet most macroscopic models still assume locality, where the desired speed only depends on local density. The nonlocal macroscopic traffic flow models that explicitly capture the ``look ahead'' and ``look behind'' nonlocal CAV dynamics remain underexplored. In this paper, we propose a Physics-informed Neu…

Critical demand in a stochastic model of flows in supply networks
Yannick Feld, Marc Barthelemy
https://arxiv.org/abs/2505.24813 https://

Critical demand in a stochastic model of flows in supply networks
Supply networks are essential for modern production, yet their critical properties remain understudied. We present a stochastic model with random production capacities to analyze material flow to a root node, focusing on topology and buffer stocks. The critical demand, where unsatisfied demand diverges, is examined mostly through numerical simulations. Without stocks, minimal production dictates behavior, making topology irrelevant. With stocks, memory effects arise, making topology crucial. In…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

This https://arxiv.org/abs/2411.06268 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Constraints and Variables Reduction for Optimal Power Flow Using Hierarchical Graph Neural Networks with Virtual Node-Splitting
Power system networks are often modeled as homogeneous graphs, which limits the ability of graph neural network (GNN) to capture individual generator features at the same nodes. By introducing the proposed virtual node-splitting strategy, generator-level attributes like costs, limits, and ramp rates can be fully captured by GNN models, improving GNN's learning capacity and prediction accuracy. Optimal power flow (OPF) problem is used for real-time grid operations. Limited timeframe motivates st…

Replaced article(s) found for q-bio.CB. https://arxiv.org/list/q-bio.CB/new
[1/1]:
- Properties of Hagen-Poiseuille flow in channel networks
A. F. Valente, R. Almeida, R. Dil\~ao

Distribution System State and Impedance Estimation Augmented with Carson's Equations
Marta Vanin, Frederik Geth, Rahmat Heidari, Dirk Van Hertem
https://arxiv.org/abs/2506.04949

Distribution System State and Impedance Estimation Augmented with Carson's Equations
The impedances of cables and lines used in (multi-conductor) distribution networks are usually unknown or approximated, and may lead to problematic results for any physics-based power system calculation, e.g., (optimal) power flow. Learning parameters from time series data is one of the few available options to obtain improved impedance models. This paper presents an approach that combines statistical learning concepts with the exploitation of domain knowledge, in the form of Carson's equations…

un_migrations: UN migration stock (2015)
A network of migration between countries, collected by the United Nations. A directed edge gives the flow of migration, and an edge property gives the number of migrants, for each given year and sex. Estimates are presented for 1990, 1995, 2000, 2005, 2010 and 2015 and are available for all countries and areas of the world. The estimates are based on official statistics on the foreign-born or the foreign population.
This network has 232 no…

Speed-Aware Network Design: A Parametric Optimization Approach
Ugo Rosolia, Marc Bataillou Almagro, George Iosifidis, Martin Gross, Georgios Paschos
https://arxiv.org/abs/2506.20216

Speed-Aware Network Design: A Parametric Optimization Approach
Network design problems have been studied from the 1950s, as they can be used in a wide range of real-world applications, e.g., design of communication and transportation networks. In classical network design problems, the objective is to minimize the cost of routing the demand flow through a graph. In this paper, we introduce a generalized version of such a problem, where the objective is to tradeoff routing costs and delivery speed; we introduce the concept of speed-coverage, which is defined…

This https://arxiv.org/abs/2405.11674 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Godunov Loss Functions for Modelling of Hyperbolic Conservation Laws
Machine learning techniques are being used as an alternative to traditional numerical discretization methods for solving hyperbolic partial differential equations (PDEs) relevant to fluid flow. Whilst numerical methods are higher fidelity, they are computationally expensive. Machine learning methods on the other hand are lower fidelity but can provide significant speed-ups. The emergence of physics-informed neural networks (PINNs) in fluid dynamics has allowed scientists to directly use PDEs fo…

Vector fields as a framework for modelling the mobility of commodities
Sima Farokhnejad, Ang\'elica S. da Mata, Mariana Macedo, Ronaldo Menezes
https://arxiv.org/abs/2506.02047

Vector fields as a framework for modelling the mobility of commodities
Commodities, including livestock, flow through trade networks globally, with trajectories that can be effectively captured using mobility pattern modelling approaches similar to those used in human mobility studies. However, documenting these movements comprehensively presents significant challenges; it can be unrealistic, costly, and may conflict with data protection regulations. As a result, mobility datasets typically contain uncertainties due to sparsity and limitations in data collection. …

Replaced article(s) found for nlin.AO. https://arxiv.org/list/nlin.AO/new
[1/1]:
- Properties of Hagen-Poiseuille flow in channel networks
A. F. Valente, R. Almeida, R. Dil\~ao