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arXiv 2023年

作者： Ni, Bo Buehler, Markus J. Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States Center for Computational Science and Engineering Schwarzman College of Computing Massachusetts Institute of Technology 77 Massachusetts Ave. CambridgeMA02139 United States

Solving mechanics problems using numerical methods requires comprehensive intelligent capability of retrieving relevant knowledge and theory, constructing and executing codes, analyzing the results, a task that has thus far mainly been reserved for humans. While emerging AI methods can provide effective approaches to solve end-to-end problems, for instance via the use of deep surrogate models or various data analytics strategies, they often lack physical intuition since knowledge is baked into the parametric complement through training, offering less flexibility when it comes to incorporating mathematical or physical insights. By leveraging diverse capabilities of multiple dynamically interacting large language models (LLMs), we can overcome the limitations of conventional approaches and develop a new class of physics-inspired generative machine learning platform, here referred to as MechAgents. A set of AI agents can solve mechanics tasks, here demonstrated for elasticity problems, via autonomous collaborations. A two-agent team can effectively write, execute and self-correct code, in order to apply finite element methods to solve classical elasticity problems in various flavors (different boundary conditions, domain geometries, meshes, small/finite deformation and linear/hyper-elastic constitutive laws, and others). For more complex tasks, we construct a larger group of agents with enhanced division of labor among planning, formulating, coding, executing and criticizing the process and results. The agents mutually correct each other to improve the overall team-work performance in understanding, formulating and validating the solution. Our framework shows the potential of synergizing the intelligence of language models, the reliability of physics-based modeling, and the dynamic collaborations among diverse agents, opening novel avenues for automation of solving engineering problems. © 2023, CC BY-NC-SA.

关键词： Elasticity

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Derivative-Free Optimization with Transformed Objective Functions (DFOTO) and the Algorithm Based on the Least Frobenius Norm Updating Quadratic Model

arXiv

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arXiv 2023年

作者： Xie, Peng-Cheng Yuan, Ya-Xiang State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Zhong Guan Cun Donglu 55 Beijing100190 China University of Chinese Academy of Sciences Zhong Guan Cun Donglu 55 Beijing100190 China

Derivative-free optimization (DFO) problems are optimization problems where the derivative information is unavailable. The least Frobenius norm updating quadratic interpolation model function is one of the essential under-determined model functions for model-based derivative-free trust-region methods. This article proposes derivative-free optimization with transformed objective functions (DFOTO) and gives a model-based trust-region method with the least Frobenius norm model. The model updating formula is based on Powell’s formula and can be easily implemented. The method shares the same framework with those for problems without transformations, and its query scheme is given. We propose the definitions related to optimality-preserving transformations to understand the interpolation model in our method when minimizing transformed objective functions. We prove the existence of model optimality-preserving transformations beyond translation transformations. The necessary and sufficient condition for such transformations is given. An interesting discovery is that, as a fundamental transformation, the affine transformation with a (non-trivial) positive multiplication coefficient is not model optimality-preserving. We also analyze the corresponding least Frobenius norm updating model and its interpolation error when the objective function is affinely transformed. The convergence property of a provable algorithmic framework containing the least Frobenius norm updating quadratic model for minimizing transformed objective functions is given. Numerical results show that our method can successfully solve most test problems with objective optimality-preserving transformations, even though some of such transformations will change the optimality of the model function. To our best knowledge, this is the first work providing the model-based derivative-free algorithm and analysis for transformed problems with the function evaluation oracle. This article also proposes the "moving-targe

关键词： Interpolation

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An Efficient Novel Approach for Iris Recognition and Segmentation Based on the Utilization of Deep Learning

An Efficient Novel Approach for Iris Recognition and Segment...

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Evolutionary Algorithms and Soft computing Techniques (EASCT), International Conference on

作者： Syed Nawaz Pasha Anitha D R Anbunathan Gobinath C Madhura G K School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Mathematics SA Engineering College Chennai India School of Computing and Information Technology Reva University Bangalore India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Iris recognition stands out as one of the most stable and accurate biometric identification systems such as facial, voice, fingerprint, handwriting, and iris recognition itself. Deep Learning (DL) plays a pivotal role in enhancing security by leveraging Convolutional Neural Networks (CNNs) for iris recognition. The acquisition of iris images and real-time processing have become focal points demanding heightened attention in the realm of DL. Iris recognition is prized for its high level of protection, making it challenging to reconstruct iris data easily from the database. The CASIA-Iris Dataset serves as a benchmark to verify the uniqueness of iris patterns and evaluate the performance of iris recognition. However, due to limitations in iris image access, the application of CNNs in the segmentation approach remains critical for iris localization and pupil boundary identification, ensuring both efficiency and specificity in iris images. The results obtained from this study reveal that the proposed CNN, utilizing DL models, achieves an impressive accuracy of 99.89% on the CASIA-Iris dataset, better performance of other existing models, such as U-Net and VGG-Net, and ensuring accurate classification.

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Joint Design of Radar Receive Filter and Unimodular ISAC Waveform with Sidelobe Level Control

arXiv

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arXiv 2025年

作者： Zhang, Kecheng Liu, Ya-Feng Wang, Zhongbin Yuan, Weijie Keskin, Musa Furkan Wymeersch, Henk Xia, Shuqiang School of System Design and Intelligent Manufacturing The Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Department of Electrical Engineering Chalmers University of Technology Gothenburg41296 Sweden ZTE Corporation The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm. © 2025, CC BY-NC-SA.

关键词： Mean square error

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Time parallelization for hyperbolic and parabolic problems

arXiv

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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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