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A Discontinuous Galerkin Finite Element Model for Compound Flood Simulations

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arXiv 2023年

作者： Wichitrnithed, Chayanon Valseth, Eirik Kubatko, Ethan J. Kang, Younghun Hudson, Mackenzie Dawson, Clint The Oden Institute for Computational Engineering and Sciences The University of Texas at Austin 201 E. 24th St. Stop C0200 AustinTX78712 United States The Department of Data Science The Norwegian University of Life Science Drobakveien 31 As1433 Norway Department of Scientific Computing and Numerical Analysis Simula Research Laboratory Kristian Augusts gate 23 Oslo0164 Norway The Department of Civil Environmental and Geodetic Engineering The Ohio State University 2070 Neil Ave ColumbusOH43210 United States

Many tropical cyclones, e.g., Hurricane Harvey (2017), have lead to significant rainfall and resulting runoff. When the runoff interacts with storm surge, the resulting floods can be greatly amplified and lead to effects that cannot be correctly modeled by simple superposition of its distinctive sources. In an effort to develop accurate numerical simulations of runoff, surge, and compounding floods, we develop a local discontinuous Galerkin method for modified shallow water equations. In this modification, nonzero sources to the continuity equation are included to incorporate rainfall into the model using parametric rainfall models from literature as well as hindcast data. The discontinuous Galerkin spatial discretization is accompanied with a strong stability preserving explicit Runge Kutta time integrator. Hence, temporal stability is ensured through the Courant-Friedrichs-Lewy (CFL) condition and we exploit the embarrassingly parallel nature of the developed method using MPI parallelization. We demonstrate the capabilities of the developed method though a sequence of physically relevant numerical tests, including small scale test cases based on laboratory measurements and large scale experiments with Hurricane Harvey in the Gulf of Mexico. The results highlight the conservation properties and robustness of the developed method and show the potential of compound flood modeling using our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Runoff

Perturbation theory for evolution of cooperation on networks

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arXiv 2022年

作者： Meng, Lingqi Masuda, Naoki Department of Mathematics University at Buffalo State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York BuffaloNY14260-5030 United States

Network structure is a mechanism for promoting cooperation in social dilemma games. In the present study, we explore graph surgery, i.e., to slightly perturb the given network, towards a network that better fosters cooperation. To this end, we develop a perturbation theory to assess the change in the propensity of cooperation when we add or remove a single edge to/from the given network. Our perturbation theory is for a previously proposed random-walk-based theory that provides the threshold benefit-to-cost ratio, (b/c)∗, which is the value of the benefit-to-cost ratio in the donation game above which the cooperator is more likely to fixate than in a control case, for any finite networks. We find that (b/c)∗ decreases when we remove a single edge in a majority of cases and that our perturbation theory captures at a reasonable accuracy which edge removal makes (b/c)∗ small to facilitate cooperation. In contrast, (b/c)∗ tends to increase when we add an edge, and the perturbation theory is not good at predicting the edge addition that changes (b/c)∗ by a large amount. Our perturbation theory significantly reduces the computational complexity for calculating the outcome of graph *** Codes 91A22, 91D30 Copyright © 2022, The Authors. All rights reserved.

关键词： Surgery

Probing quasigeostrophic turbulence via complex networks

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Physical Review Fluids 2025年第5期10卷 054402-054402页

作者： V. R. Krishna Priya Snehal Sunil Patil Somnath Roy Konduri Aditya Rajaram Lakkaraju Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur West Bengal-721302 India TuRbulent Interfaces And Dispersion (TRIAD) Department of Mechanical Engineering Indian Institute of Technology Kharagpur West Bengal-721302 India Department of Computational and Data Sciences Indian Institute of Science Bangalore-560012 India

Using statistical analysis and complex networks, we study the role of inverse Rossby deformation radius, λ, on freely decaying quasi-geostrophic turbulence dynamics. We find that the area fraction occupied by vortices remains constant over an extended duration for all values of λ. We establish an energy spectrum scaling based on a self-similar distribution of vortices of area A during this period. Our findings suggest that the vortex number density follows the relationship n(A)∼NvA−p, and the inertial range energy spectra scales as k−7+2p, where Nv represents the number of vortices and p depends on λ. To investigate vortical interactions, we construct a spatiotemporal weighted network. Our analysis uncovers two distinct strength distributions: (1) a briefly observed scale-free distribution resulting from nonlocal interactions when λ≪k0, and (2) a truncated scale-free distribution due to local interactions over an extended period when λ≫k0, where k0 denotes the initial characteristic wave number at which the total energy is at its maximum. Both networks are robust against random disturbances but are vulnerable to targeted attacks on their hubs. Additionally, we found that these network hubs contribute to the large-scale energy spectra, which can be reconstructed using only 1% of the total degrees of freedom in a quasi-geostrophic turbulent field.

关键词： Dynamics

Spreading dynamics of information on online social networks

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arXiv 2025年

作者： Meng, Fanhui Xie, Jiarong Sun, Jiachen Xu, Cong Zeng, Yutian Wang, Xiangrong Jia, Tao Huang, Shuhong Deng, Youjin Hu, Yanqing School of Systems Science and Engineering Sun Yat-sen University Guangzhou510275 China Center for Computational Communication Research Beijing Normal University Zhuhai519087 China School of Journalism and Communication Beijing Normal University Beijing100875 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Department of Statistics and Data Science College of Science Southern University of Science and Technology Shenzhen518055 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China College of Computer and Information Science Southwest University Chongqing400715 China Institute of Neuroscience Technical University of Munich Munich80802 Germany Department of Modern Physics University of Science and Technology of China Hefei230026 China MinJiang Collaborative Center for Theoretical Physics College of Physics and Electronic Information Engineering Minjiang University Fuzhou350108 China Center for Complex Flows and Soft Matter Research Southern University of Science and Technology Shenzhen518055 China

Social media is profoundly changing our society with its unprecedented spreading power. Due to the complexity of human behaviors and the diversity of massive messages, the information spreading dynamics are complicated, and the reported mechanisms are different and even controversial. Based on data from mainstream social media platforms, including WeChat, Weibo, and Twitter, cumulatively encompassing a total of 7.45 billion users, we uncover a ubiquitous mechanism that the information spreading dynamics are basically driven by the interplay of social reinforcement and social weakening effects. Accordingly, we propose a concise equation, which, surprisingly, can well describe all the empirical large-scale spreading trajectories. Our theory resolves a number of controversial claims and satisfactorily explains many phenomena previously observed. It also reveals that the highly clustered nature of social networks can lead to rapid and high-frequency information bursts with relatively small coverage per burst. This vital feature enables social media to have a high capacity and diversity for information dissemination, beneficial for its ecological development. © 2025, CC BY.

关键词： Tweets

SR-CurvANN: Advancing 3D surface reconstruction through curvature-aware neural networks

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Computers & Graphics 2025年 130卷

作者： Marina Hernández-Bautista Francisco J. Melero Department of Computer Science and Artificial Intelligence University of Granada 18071 Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) Spain Department of Software Engineering University of Granada 18071 Granada Spain

Incomplete or missing data in three-dimensional (3D) models can lead to erroneous or flawed renderings, limiting their usefulness in applications such as visualization, geometric computation, and 3D printing. Conventional surface-repair techniques often fail to infer complex geometric details in missing areas. Neural networks successfully address hole-filling tasks in 2D images using inpainting techniques. The combination of surface reconstruction algorithms, guided by the model’s curvature properties and the creativity of neural networks in the inpainting processes, should provide realistic results in the hole completion task. In this paper, we propose a novel method entitled SR-CurvANN (Surface Reconstruction Based on Curvature-Aware Neural Networks) that incorporates neural network-based 2D inpainting to effectively reconstruct 3D surfaces. We train the neural networks with images that represent planar representations of the curvature at vertices of hundreds of 3D models. Once the missing areas have been inferred, a coarse-to-fine surface deformation process ensures that the surface fits the reconstructed curvature image. Our proposal makes it possible to learn and generalize patterns from a wide variety of training 3D models, generating comprehensive inpainted curvature images and surfaces. Experiments conducted on 959 models with several holes have demonstrated that SR-CurvANN excels in the shape completion process, filling holes with a remarkable level of realism and precision.

关键词： Shape completion Curvature representation Parametrization Inpainting

A Survey on Distributed Evolutionary Computation

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arXiv 2023年

作者： Chen, Wei-Neng Wei, Feng-Feng Zhao, Tian-Fang Tan, Kay Chen Zhang, Jun The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Guangdong-Hong Kong Joint Innovative Platform of Big Data and Computational Intelligence South China University of Technology Guangzhou510006 China The School of Journalism and Communication Jinan University Guangzhou510006 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Electrical and Electronic Engineering Hanyang University Ansan15588 Korea Republic of

The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by parallel computing systems can significantly improve the efficiency and scalability of EC. On the other hand, data are collected and processed in a distributed manner, which brings a novel development direction and new challenges to EC. In this paper, we intend to give a systematic review on distributed EC (DEC). First, a new taxonomy for DEC is proposed from top design mechanism to bottom implementation mechanism. Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment. Second, we clarify two major purposes of DEC, i.e., improving efficiency through parallel processing for centralized optimization and cooperating distributed individuals/sub-populations with partial information to perform distributed optimization. Third, noting that the latter purpose of DEC is an emerging and attractive trend for EC with the booming of spatially distributed paradigms, this paper gives a systematic definition of the distributed optimization and classifies it into dimension distributed-, data distributed-, and objective distributed-optimization problems. Formal formulations for these problems are provided and various DEC studies on these problems are reviewed. We also discuss challenges and potential research directions, aiming to enlighten the design of DEC and pave the way for future developments. Copyright © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

Machine learning-based prediction of cognitive outcomes in de novo Parkinson's disease

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Research Square

Research Square 2022年

作者： Harvey, Joshua Reijnders, Rick A. Cavill, Rachel Duits, Annelien Köhler, Sebastian Eijssen, Lars Rutten, Bart P.F. Shireby, Gemma Torkamani, Ali Creese, Byron Leentjens, Albert F.G. Lunnon, Katie Pishva, Ehsan University of Exeter Medical School University of Exeter Exeter United Kingdom Maastricht University Maastricht Netherlands Department of Data Science and Knowledge Engineering FSE Maastricht University Maastricht Netherlands Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States

Cognitive impairment is a debilitating symptom in Parkinson’s disease (PD). We aimed to establish an accurate multivariate machine learning (ML) model to predict cognitive outcome in newly diagnosed PD cases from the Parkinson’s Progression Markers Initiative (PPMI). Annual cognitive assessments over an eight-year time span were used to define two cognitive outcomes of i) cognitive impairment, and ii) dementia conversion. Selected baseline variables were organized into three subsets of clinical, biofluid and genetic/epigenetic measures and tested using four different ML algorithms. Irrespective of the ML algorithm used, the models consisting of the clinical variables performed best and showed better prediction of cognitive impairment outcome over dementia conversion. We observed a marginal improvement in the prediction performance when clinical, biofluid, and epigenetic/genetic variables were all included in one model. Several cerebrospinal fluid measures and an epigenetic marker showed high predictive weighting in multiple models when included alongside clinical variables. © 2022, CC BY.

关键词： Genome

BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications

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arXiv 2023年

作者： Lin, Jiatai Han, Guoqiang Xu, Xuemiao Liang, Changhong Wong, Tien-Tsin Chen, C.L. Philip Liu, Zaiyi Han, Chu The School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510006 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Southern Medical University Guangzhou510080 China School of Computer Science and Engineering South China University of Technology State Key Laboratory of Subtropical Building Science Ministry of Education Key Laboratory of Big Data and Intelligent Robot Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

Class activation mapping (CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation (WSSS) and object localization (WSOL). It is the weighted aggregation of the feature maps by activating the high class-relevance ones. Current CAM methods achieve it relying on the training outcomes, such as predicted scores (forward information), gradients (backward information), etc. However, when with small-scale data, unstable training may lead to less effective model outcomes and generate unreliable weights, finally resulting in incorrect activation and noisy CAM seeds. In this paper, we propose an outcome-agnostic CAM approach, called BroadCAM, for small-scale weakly supervised applications. Since broad learning system (BLS) is independent to the model learning, BroadCAM can avoid the weights being affected by the unreliable model outcomes when with small-scale data. By evaluating BroadCAM on VOC2012 (natural images) and BCSS-WSSS (medical images) for WSSS and OpenImages30k for WSOL, BroadCAM demonstrates superior performance than existing CAM methods with small-scale data (less than 5%) in different CNN architectures. It also achieves SOTA performance with large-scale training data. Extensive qualitative comparisons are conducted to demonstrate how BroadCAM activates the high class-relevance feature maps and generates reliable CAMs when with small-scale training data. © 2023, CC BY.

关键词： Learning systems

On Generalizations of the Nonwindowed Scattering Transform

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arXiv 2022年

作者： Chua, Albert Hirn, Matthew Little, Anna Department of Mathematics Michigan State University East LansingMI48824 United States Department of Computational Mathematics Science & Engineering Michigan State University East LansingMI48824 United States Center for Quantum Computing Science & Engineering Michigan State University East LansingMI48824 United States Department of Mathematics the Utah Center For Data Science University of Utah Salt Lake CityUT84112 United States

In this paper, we generalize finite depth wavelet scattering transforms, which we formulate as Lq(Rn) norms of a cascade of continuous wavelet transforms (or dyadic wavelet transforms) and contractive nonlinearities. We then provide norms for these operators, prove that these operators are well-defined, and are Lipschitz continuous to the action of C2 diffeomorphisms in specific cases. Lastly, we extend our results to formulate an operator invariant to the action of rotations R ∈ SO(n) and an operator that is equivariant to the action of rotations of R ∈ SO(n). © 2022, CC BY.

关键词： Wavelet transforms