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arXiv 2025年

作者： Gander, Martin J. Wu, Shu-Lin Zhou, Tao Department of Mathematics University of Geneva CP64 Geneva 41211 Switzerland School of Mathematics and Statistics Northeast Normal University Changchun130024 China Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences China

Time parallelization, also known as PinT (Parallel-in-Time) is a new research direction for the development of algorithms used for solving very large scale evolution problems on highly parallel computing architectures. Despite the fact that interesting theoretical work on PinT appeared as early 1964, it was not until 2004, when processor clock speeds reached their physical limit, that research in PinT took off. A distinctive characteristic of parallelization in time is that information flow only goes forward in time, meaning that time evolution processes seem necessarily to be sequential. Nevertheless, many algorithms have been developed over the last two decades to do PinT computations, and they are often grouped into four basic classes according to how the techniques work and are used: shooting-type methods;waveform relaxation methods based on domain decomposition;multigrid methods in space-time;and direct time parallel methods. However, over the past few years, it has been recognized that highly successful PinT algorithms for parabolic problems struggle when applied to hyperbolic problems. We focus in this survey therefore on this important aspect, by first providing a summary of the fundamental differences between parabolic and hyperbolic problems for time parallelization. We then group PinT algorithms into two basic groups: the first group contains four effective PinT techniques for hyperbolic problems, namely Schwarz Waveform Relaxation with its relation to Tent Pitching;Parallel Integral Deferred Correction;ParaExp;and ParaDiag. While the methods in the first group also work well for parabolic problems, we then present PinT methods especially designed for parabolic problems in the second group: Parareal: the Parallel Full Approximation Scheme in Space-Time;Multigrid Reduction in Time;and Space-Time Multigrid. We complement our analysis with numerical illustrations using four time-dependent PDEs: the heat equation;the advection-diffusion equation;Burgers’ equa

关键词： Wave equations

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IoT-Enabled Precision Irrigation for Coconut Plantations: Enhancing Growth and Sustainability

IoT-Enabled Precision Irrigation for Coconut Plantations: En...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： M.Jaganmohan Reddy V. Ramu T. Grace Shalini R. Vincysahaba M. Devaki A. Athiraja Department of CSE (AIML) Malla Reddy Engineering College Secunderabad Telangana India Department of CSE(AI&ML) Kakatiya Institute of Technology and Science Hanamkonda Telangana India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Artificial Intelligence and Data Science St.Joseph college of engineering Chennai Tamil Nadu India Department of EEE Velammal college of engineering and technology Madurai Tamil Nadu India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

In agriculture, precision irrigation plays a pivotal role in optimizing crop growth and resource utilization. In this study, must proposean Internet of Things based irrigation remote monitoring system tailored for enhancing coconut plant growth. The system integrates sensor technology, Internet of Things connectivity, and data analytics to provide real-time monitoring and control of irrigation parameters such as soilmoisture, temperature, and humidity. Leveraging a network of sensors deployed in the coconut plantation, the system collects and analyzes environmental data to optimize irrigation schedules and ensure optimal growing conditions for coconut plants. Through a user-friendly interface accessible via web or mobile applications, farmers can remotely monitor the status of their plantation and adjust irrigation settings as ***, the system incorporates predictive analytics capabilities to anticipate irrigation needs based on historical data and weather forecasts, thereby enhancing water efficiency and crop productivity. Field trials demonstrate the effectiveness of this Internet of Things based irrigation system in improving coconut plant growth while reducing water consumption and operational costs. This findings underscore the potential of Internet of Things technologies in revolutionizing agricultural practices, facilitating sustainable crop management, and enhancing food security in coconut-producing regions.

关键词： Temperature sensors Productivity Irrigation Costs Crops Plantations Food security Internet of Things Sustainable development Predictive analytics

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A Mathematical Aspect of Bloch's Theorem

arXiv

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arXiv 2024年

作者： Li, Yan Yang, Bin Zhou, Aihui LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

In this paper, by studying a class of 1-D Sturm-Liouville problems with periodic coefficients, we show and classify the solutions of periodic Schrodinger equations in a multidimensional case, which tells that not all the solutions are Bloch solutions. In addition, we also provide several properties of the solutions and quasimomenta and illustrate the relationship between bounded solutions and Bloch solutions. Copyright © 2024, The Authors. All rights reserved.

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Lyapunov exponents and Lagrangian chaos suppression in compressible homogeneous isotropic turbulence

arXiv

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arXiv 2023年

作者： Yu, Haijun Fouxon, Itzhak Wang, Jianchun Li, Xiangru Yuan, Li Mao, Shipeng Mond, Michael NCMIS & LSEC Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Beijing100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing100049 China Department of Mechanical Engineering Ben-Gurion University of the Negev Beer-Sheva84105 Israel Department of Computational Science and Engineering Yonsei University Seoul03722 Korea Republic of Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen518055 China

We study Lyapunov exponents of tracers in compressible homogeneous isotropic turbulence at different turbulent Mach number Mt and Taylor-scale Reynolds number Reλ. We demonstrate that statistics of finite-time Lyapunov exponents have the same form as in incompressible flow due to density-velocity coupling. The modulus of the smallest Lyapunov exponent λ3 provides the principal Lyapunov exponent of the time-reversed flow, which is usually wrong in a compressible flow. This exponent, along with the principal Lyapunov exponent λ1, determines all the exponents due to vanishing of the sum of all Lyapunov exponents. Numerical results by high-order schemes for solving the Navier-Stokes equations and tracking particles verify these theoretical predictions. We found that: 1) The largest normalized Lyapunov exponent λ1τη, where τη is the Kolmogorov timescale, is a decreasing function of Mt. Its dependence on Reλ is weak when the driving force is solenoidal, while it is an increasing function of Reλ when the solenoidal and compressible forces are comparable. Similar facts hold for |λ3|, in contrast with well-studied short-correlated model;2) The ratio of the first two Lyapunov exponents λ1/λ2 decreases with Reλ, and is virtually independent of Mt for Mt ≤ 1 in the case of solenoidal force but decreases as Mt increases when solenoidal and compressible forces are comparable;3) For purely solenoidal force, λ1 : λ2 : λ3 ≈ 4: 1: −5 for Reλ > 80, which is consistent with incompressible turbulence studies;4) The ratio of dilation-to-vorticity is a more suitable parameter to characterize Lyapunov exponents than Mt © 2023, CC BY-NC-ND.

关键词： Navier Stokes equations

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Meson spectroscopy from spectral densities in lattice gauge theories

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Physical Review D 2024年第7期110卷 074509页

作者： Ed Bennett Luigi Del Debbio Niccolò Forzano Ryan C. Hill Deog Ki Hong Ho Hsiao Jong-Wan Lee C.-J. David Lin Biagio Lucini Alessandro Lupo Maurizio Piai Davide Vadacchino Fabian Zierler Swansea Academy of Advanced Computing Higgs Centre for Theoretical Physics School of Physics and Astronomy Department of Physics Faculty of Science and Engineering School of Physics and Astronomy Department of Physics Extreme Physics Institute Institute of Physics Particle Theory and Cosmology Group Center for Theoretical Physics of the Universe Centre for High Energy Physics Centre for Theoretical and Computational Physics Department of Mathematics Faculty of Science and Engineering Université de Toulon CNRS CPT Marseille France Centre for Mathematical Sciences

Spectral densities encode nonperturbative information that enters the calculation of a plethora of physical observables in strongly coupled field theories. Phenomenological applications encompass aspects of standard-model hadronic physics, observable at current colliders, as well as correlation functions characterizing new physics proposals, testable in future experiments. By making use of numerical data produced in a Sp(4) lattice gauge theory with matter transforming in an admixture of fundamental and 2-index antisymmetric representations of the gauge group, we perform a systematic study to demonstrate the effectiveness of recent technological progress in the reconstruction of spectral *** this purpose, we write and test new software packages that use energy-smeared spectral densities to analyze the mass spectrum of mesons. We assess the effectiveness of different smearing kernels and optimize the smearing parameters to the characteristics of available lattice ensembles. For concreteness, we analyze the Sp(4) lattice gauge theory with matter transforming in an admixture of fundamental and 2-index antisymmetric representations of the gauge group. We generate new ensembles for the theory in consideration, with lattices that have a longer extent in the time direction with respect to the spatial ones. We run our tests on these ensembles, obtaining new results about the spectrum of light mesons and their excitations. We make available our algorithm and software for the extraction of spectral densities, that can be applied to theories with other gauge groups, including the theory of strong interactions (QCD) governing hadronic physics in the standard model.

关键词： Hybrid Monte Carlo algorithm Lattice field theory

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Improving the Noise Estimation of Latent Neural Stochastic Differential Equations

arXiv

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arXiv 2024年

作者： Heck, Linus Gelbrecht, M. Schaub, M. Boers, N. Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Earth System Modelling School of Engineering and Design TU Munich Germany Computational Network Science Dept of Computer Science RWTH Aachen University Germany Department of Computer Science RWTH Aachen University Germany Complexity Science Potsdam Institute for Climate Impact Research Potsdam Germany Department of Mathematics and Statistics University of Exeter Exeter United Kingdom

Latent neural stochastic differential equations (SDEs) have recently emerged as a promising approach for learning generative models from stochastic time series data. However, they systematically underestimate the noise level inherent in such data, limiting their ability to capture stochastic dynamics accurately. We investigate this underestimation in detail and propose a straightforward solution: by including an explicit additional noise regularization in the loss function, we are able to learn a model that accurately captures the diffusion component of the data. We demonstrate our results on a conceptual model system that highlights the improved latent neural SDE’s capability to model stochastic bistable dynamics. © 2024, CC BY-NC-ND.

关键词： Stochastic models

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Prediction of Rental Demands using different Machine Learning Algorithms

Prediction of Rental Demands using different Machine Learnin...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： S Ravikumar K Vijay S. Pavithra S Prithi Sriram Kannan Department of Computer Science and Engineeirng Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai School of Computer science and Engineering VIT university Chennai Department of Computational Intelligence School of Computing SRM IST Kattankulathur Sri Sivasubramaniya Nadar College of Engineering Chennai Tamil Nadu

A Bike rental expectation conjectures the interest for bikes rentals in reliance of climate conditions like the temperature and calendric data. In bike rental frameworks, it is vital that the managers have to realize the number of bikes to be placed in each and every station. This include empowers them in or-ganizing the necessary number of bikes at each station and choose whether a specific station needs to have additional number of bikes or it is not re-quired. In this venture we will foresee the no of bikes required for explicit climate and climatic conditions.

关键词： Training Temperature distribution Machine learning algorithms Urban areas Humidity Forestry Predictive models

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Energy-Efficient Routing in Underwater Wireless Sensor Networks Through Cluster-Dragonfly Optimization

Energy-Efficient Routing in Underwater Wireless Sensor Netwo...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Anitha D Saif O. Husain Srikanth Velpula Rajani N R. Dineshkumar Department of Computational Intelligence School of computing SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electrical and Electronics Engineering SR University Warangal India Dept.of ECE Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In this paper, develop a power control scheme for underwater wireless sensor networks that takes into account the impact of communication ranges on network performance, including energy utilization and delay overhead. The primary objective of our scheme is to assign a differentiated optimal communication radius for each sensor node, enhancing overall network efficiency. The proposed method utilizes energy-aware Cluster-Dragonfly Optimization (CDFO), but it might face challenges in handling large-scale optimization, and the scalability of the algorithm may be limited when dealing with high-dimensional or complex optimization. Although the proposed CDFO method exhibits high performance compared to existing methods, such as Self-Adaptive Glow Worm Swarm Optimization (SA-GWO), Improved Coyote Optimization Algorithm (ICOA), Hybrid Cat Cheetah Optimization algorithm (HCCOA), and Chaotic Search-and-Rescue Optimization (CSRO), it may still face challenges in terms of handling large-scale optimization. Nevertheless, the proposed CDFO yields impressive results, achieving a Packet Delivery Ratio (PDR) of 99%, Packet Loss Ratio (PLR) of 4%, Energy Consumption of 9%, and Alive Node count of 330. These outcomes outperform existing methods in the context of Underwater WSN.

关键词： Wireless sensor networks Energy consumption Chaotic communication Clustering algorithms Packet loss Routing Particle swarm optimization

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BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-inspired Materials

arXiv

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arXiv 2023年

作者： Luu, Rachel K. Buehler, Markus J. Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Materials Science and Engineering Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States

The study of biological materials and bio-inspired materials science is well established;however, surprisingly little knowledge has been systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model was finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further enhanced with enhanced reasoning ability, as well as with retrieval-augmented generation to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has been shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model showed impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains. © 2023, CC BY-NC-SA.

关键词： Biological materials

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Robust Sensor Fusion Algorithms for IoT

Robust Sensor Fusion Algorithms for IoT

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Sakthitharan Subramanian Gandrakoti Lokeshwari S. Jagadeesan S. Leela V. G Pratheep Murugesan Venkatasudhahar Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology School of Computing Chennai Tamil Nadu India Department of Cybersecurity Geethanjali College of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Nandha Engineering college(Autonomous) Erode Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Mechatronics Engineering Kongu Engineering College Perundurai Tamil Nadu India Department of Mechanical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Sensor fusion is a critical component of the IoT ecosystem, as it combines a variety of data streams from multiple sensors to enhance the accuracy, reliability, and real-time decision-making. This research concentrates on the development of robust sensor fusion algorithms that are capable of effectively managing the heterogeneity, noise, and uncertainty that are inherent in data from Internet of Things (IoT) sensors. Our primary focus is on the ability of these algorithmic methodologies to manage data from multi-modal sensors. We investigate algorithms that are based on deep learning, Bayesian inference, and Kalman filtering. The proposed solutions aim to improve the performance of systems in sectors that are influenced by the Internet of Things (IoT), including healthcare, smart cities, industrial automation, and autonomous cars. This investigation investigates the scalability, energy efficiency, and computational burden of the Internet of Things (IoT) in the context of limited resources. Through simulations and practical applications, we demonstrate the enhancement of data precision, defect tolerance, and overall system dependability by these robust sensor fusion techniques.

关键词： Deep learning Uncertainty Smart cities Sensor fusion Inference algorithms Bayes methods Internet of Things Reliability Kalman filters Streams

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