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Data-driven model discovery with Kolmogorov-Arnold networks

Physical Review Research 2025年第2期7卷 023037-023037页

作者： Shirin Panahi Mohammadamin Moradi Erik M. Bollt Ying-Cheng Lai School of Electrical Computer and Energy Engineering Arizona State University Tempe Arizona 85287 USA Department of Electrical and Computer Engineering Clarkson Center for Complex Systems Science Clarkson University Potsdam New York 13699 USA Department of Physics Arizona State University Tempe Arizona 85287 USA

Data-driven model discovery of complex dynamical systems can be done using sparse optimization, but it has a fundamental limitation: sparsity in that the underlying governing equations of the system contain only a small number of elementary mathematical functions. Examples where sparse optimization fails abound, including the classic Ikeda or optical-cavity map in nonlinear dynamics, as well as a wide variety of ecosystems. Another approach is based on machine learning, e.g., deep neural networks, which excels at capturing system behavior from data but functions as black boxes, offering little insight into how inputs influence the outputs. We propose a general model-discovery framework based on the Kolmogorov-Arnold networks (KANs) that are not constrained by the sparsity condition. The KAN framework with a simple structure is capable of accurately capturing the complex behavior of dynamical systems that do not meet the sparsity requirement while offering greater interpretability compared to conventional neural networks. This interpretability provides insights into the dynamics generating the data, which are typically inaccessible in traditional black-box function approximation methods. We demonstrate nonuniqueness in that a large number of approximate models of the system can be found that generate the same invariant set with the true statistics such as the Lyapunov exponents and Kullback–Leibler divergence. An analogy to shadowing of numerical trajectories in chaotic systems is pointed out.

关键词： Deep neural networks

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Design and Implementation of a Flexible Wireless Sensor Network for Environmental Monitoring with in Digital Cities Framework

Design and Implementation of a Flexible Wireless Sensor Netw...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： B. Sai Manogna R Gowri Shankar Rao K Ravi Chythanya Navamani C K. S. Raghuram J. Samson Isaac Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad India Department of Physics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India Department of Computer Science and Engineering Nandha Engineering College (Autonomous) Erode India Department of Mechanical Engineering Vignan's Institute of Information Technology (A) Visakhapatnam India Department of Biomedical Engineering School of Engineering and Technology Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The paper highlights a Wireless Sensor Network (WSN) implementation for environmental monitoring, emphasizing cost-effectiveness and adaptability. APIs designed to handle values instead of voltage levels increase system resilience, while open standards promote security, cost efficiency, and auditability. The data shows a diurnal temperature range from 20°C (night) to 38°C (day), stable humidity at 38%, and UV radiation peaking at 6 (day) and dropping to 1.5 (night). Smoke levels are consistently high at 490 μg/m 3 , while toxic gas concentrations slightly rise to 0.5 ppm during the day from 0.45 ppm at night. Fire metrics remain low and stable. These findings highlight the need for UV protection, air quality controls, and consistent fire safety measures. The system achieves effective environmental monitoring while remaining adaptable for future needs, underscoring its value in scalable, cost-efficient, and sustainable public management solutions.

关键词： Temperature sensors Wireless sensor networks Temperature distribution Technological innovation Zigbee User interfaces Environmental monitoring Security Sustainable development Standards

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On the Importance of Error Mitigation for Quantum Computation

arXiv

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arXiv 2025年

作者： Aharonov, Dorit Alberton, Ori Arad, Itai Atia, Yosi Bairey, Eyal Brakerski, Zvika Cohen, Itsik Golan, Omri Gurwich, Ilya Kenneth, Oded Leviatan, Eyal Lindner, Netanel H. Melcer, Ron Aharon Meyer, Adiel Schul, Gili Shutman, Maor Qedma Quantum Computing Tel Aviv Israel The Benin School of Computer Science and Engineering Hebrew University Jerusalem Israel Centre for Quantum Technologies National University of Singapore Singapore Faculty of Mathematics and Computer Science Weizmann Institute of Science Israel Department of Physics Technion Haifa Israel

Quantum error mitigation (EM) is a family of hybrid quantum-classical methods for eliminating or reducing the effect of noise and decoherence on quantum algorithms run on quantum hardware, without applying quantum error correction (EC). While EM has many benefits compared to EC, specifically that it requires no (or little) qubit overhead, this benefit comes with a painful price: EM seems to necessitate an overhead in quantum run time which grows as a (mild) exponent [1–4]. Accordingly, recent results show that EM alone cannot enable exponential quantum advantages (QAs), for an average variant of the expectation value estimation problem [5]. These works raised concerns regarding the role of EM in the road map towards QAs. We aim to demystify the discussion, and provide a clear picture of the role of EM in achieving QAs, both in the near and long term. We first propose a clear distinction between finite QA and asymptotic QA, which is crucial to the understanding of the question, and present the notion of circuit volume boost, which we claim is an adequate way to quantify the benefits of EM. Using these notions we can argue straightforwardly that EM is expected to have a significant role in achieving QAs. Specifically, that EM is likely to be the first error reduction method for useful finite QAs, before EC;that the first such QAs are expected to be achieved using EM in the very near future;and that EM is expected to maintain its important role in quantum computation even when EC will be routinely used – for as long as high-quality qubits remain a scarce resource. © 2025, CC BY.

关键词： Qubits

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Obstacle Avoidance in Routing with GNR Interconnect

Obstacle Avoidance in Routing with GNR Interconnect

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Devices for Integrated Circuit (DevIC)

作者： Subrata Das Arighna Deb Soumya Pandit Debesh Kumar Das Dept. of Computer Science & Engineering Supreme Knowledge Foundation Group of Institutions Hooghly West Bengal India School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Institute of Radio Physics and Electronics University of Calcutta Kolkata India Dept. of Computer Science & Engineering Jadavpur University Kolkata India

ISBN: (数字)9798350391107

ISBN: (纸本)9798350391114

During the routing phase of VLSI physical design, obstacles often arise along the routing paths due to previously established connections or other constraints. These obstacles pose significant challenges for VLSI designers in constructing efficient routing trees that navigate around them. Graphene nanoribbon interconnects, which are limited to bending angles of 0°, 60°, and 120°, further complicate this process. The routing cost for graphene nanoribbon interconnects depends on both the interconnect length and the bending angles involved. In this work, we propose a cost-minimization technique that leverages graphene nanoribbon interconnects to determine optimal routing paths for a given source node, a set of sink terminals, and multiple rectangular obstacles.

关键词： Costs Navigation Graphene Integrated circuit interconnections Very large scale integration Bending Routing Minimization Nanoscale devices Physical design

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Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

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International Conference on Mechatronics (ICM)

作者： Gaku Takei Hidemine Obara Ryo Haneda Takahiro Nozaki Graduate School of Science and Technology Keio University Yokohama Japan Department of Physics Electrical and Computer Engineering Yokohama National University Yokohama Japan Department of System Design Engineering Keio University Yokohama Japan

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths.

关键词： Coils Low voltage Simulation Switches Multilevel inverters Motors Topology Sensors Voltage control Motion control

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Exploring quantum control landscape and solution space complexity through optimization algorithms and dimensionality reduction

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Scientific Reports 2025年第1期15卷 1-13页

作者： Fentaw, Haftu W. Campbell, Steve Caton, Simon School of Computer Science University College Dublin Dublin Ireland School of Physics University College Dublin Dublin Ireland Centre for Quantum Engineering Science and Technology University College Dublin Dublin Ireland

Understanding the quantum control landscape (QCL) is important for designing effective quantum control strategies. In this study, we analyze the QCL for a single two-level quantum system (qubit) using various control strategies. We employ Principal Component Analysis (PCA), to visualize and analyze the QCL for higher dimensional control parameters. Our results indicate that dimensionality reduction techniques such as PCA, can play an important role in understanding the complex nature of quantum control in higher dimensions. Evaluations of traditional control techniques and machine learning algorithms reveal that Genetic Algorithms (GA) outperform Stochastic Gradient Descent (SGD), while Q-learning (QL) shows great promise compared to Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO). Additionally, our experiments highlight the importance of reward function design in DQN and PPO demonstrating that using immediate reward results in improved performance rather than delayed rewards for systems with short time steps. A study of solution space complexity was conducted by using Cluster Density Index (CDI) as a key metric for analyzing the density of optimal solutions in the landscape. The CDI reflects cluster quality and helps determine whether a given algorithm generates regions of high fidelity or not. Our results provide insights into effective quantum control strategies, emphasizing the significance of parameter selection and algorithm optimization. © The Author(s) 2025.

关键词： Genetic Algorithms (GA) Principal Component Analysis (PCA) Quantum Control Landscape (QL) Reinforcement Learning (RL) Stochastic Gradient Descent (SGD)

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NOMA-Based Ze-RIS Empowered Backscatter Communication with Energy-Efficient Resource Management

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IEEE Transactions on Communications 2025年

作者： Asif, Muhammad Bao, Xu Ihsan, Asim Khan, Wali Ullah Li, Xingwang Chatzinotas, Symeon Dobre, Octavia A. The School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China The Department of Electrical Engineering University of Cambridge Cambridge United Kingdom University of Luxembourg Luxembourg City1855 Luxembourg The School of Physics and Electronic Information Engineering Henan Polytechnic University the Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China The Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Memorial University St. John’sNLA1B 3X9 Canada

This manuscript introduces a novel energy-efficient optimization strategy for a zero-energy reconfigurable intelligent reflecting surface (Ze-RIS) supported backscatter communication system employing non-orthogonal multiple access (NOMA). The central objective is to maximize the energy efficiency of the system by optimizing the several key parameters, including the amplitude reflection coefficient of Ze-RIS, the reflection coefficients of the backscatter tags, transmit beamforming at the base station, and passive beamforming at the Ze-RIS node, while incorporating a practical non-linear energy harvesting model both for the Ze-RIS and backscatter nodes. The proposed algorithm addresses the complex non-convex problem through three stages. Firstly, the transmit beamforming vectors are determined by leveraging the semi-definite programming and successive-convex approximation, while handling the rank-1 constraint with the semi-definite relaxation. Secondly, we determine the amplitude reflection coefficient of Ze-RIS by leveraging the monotonicity property of the objective function. Simultaneously, we compute the reflection coefficients of backscatter tags using the Dinkelbach algorithm, Lagrange duality, and the sub-gradient method. Thirdly, we compute passive beamforming using successive-convex approximation and semi-definite programming techniques, achieving a rank-1 solution through the penalty-based method. Finally, the numerical simulations confirm the effectiveness of the proposed approach, demonstrating its superiority over the benchmark competitors with rapid convergence within a few iterations. © 1972-2012 IEEE. All rights reserved.

关键词： Beamforming

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Automated Seizure Detection and Diagnosis using Cascaded Reflection Equivariant Visual Attention Quantum Neural Network with Dung Beetle Optimization Algorithm Applied to EEG Signals

Automated Seizure Detection and Diagnosis using Cascaded Ref...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： B. Jagadeeswari Ranjitham. G Prasanta Kumar Parida L. Guganathan Megha D Bengalur Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Coimbatore Tamil Nadu India School of Rural Management KIIT University Bhubaneswar Odisha India Department of Physics Saveetha School of Engineering Saveetha Insitute of Medical and Technical Science (SIMATS) Thandalam Chennai Tamil Nadu India Department of Computer Science and Engineering Presidency University Bengaluru Karnataka India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Automated seizure detection and diagnosis for EEG signals refers to using deep learning algorithms and computational methods to analyze electroencephalogram data, identifying patterns that indicate epileptic seizures. We can assert that these systems speed up the process of correct diagnosis reducing the differentiation between the seizures and normal brain activity, thus improving the overall clinical work and patients’ outcomes. Conventional techniques fail to give precise and rapid results relating to complicated patterns of seizures. To address these challenges; this study introduces the Cascaded Reflection Equivariant Visual Attention Quantum Neural Network with Dung Beetle Optimization Algorithm (CasREVAQNNet+DBOA) framework for automated seizure detection and diagnosis in EEG signals. The proposed method leverages the CHB-MIT dataset, with initial preprocessing performed using Gradient Domain Guided Filtering (GDGF) to enhance critical EEG patterns. Feature extraction is carried out using DenseNet-121, which facilitates the effective representation of significant EEG characteristics. For classification, the Cascaded Reflection Equivariant Visual Attention Quantum Neural Network (CasREVAQNNet) is utilized, with performance further optimized by the Dung Beetle Optimization Algorithm (DBOA) to ensure accurate and reliable seizure detection (SD). The CasREVAQNNet+DBOA framework is implemented in Python and evaluated on the CHB-MIT dataset. The proposed approach yields excellent outcomes: accuracy of 99.8% and specificity of 98.6% which are significantly higher in contrast to the current techniques. The outcomes of the suggested method enhance accuracy, reduce false detection rates, and accelerate diagnosis, making it a potentially useful tool for reliable and efficient seizure detection in clinical practice.

关键词： Visualization Accuracy Filtering Brain modeling Electroencephalography Reflection Classification algorithms Reliability Optimization Biological neural networks

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Generative Physical AI in Vision: A Survey

arXiv

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arXiv 2025年

作者： Liu, Daochang Zhang, Junyu Dinh, Anh-Dung Park, Eunbyung Zhang, Shichao Xu, Chang School of Physics Mathematics and Computing The University of Western Australia CrawleyWA6009 Australia School of Computer Science and Engineering Central South University Changsha410083 China School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Department of Artificial Intelligence Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China

Generative Artificial Intelligence (AI) has rapidly advanced the field of computer vision by enabling machines to create and interpret visual data with unprecedented sophistication. This transformation builds upon a foundation of generative models to produce realistic images, videos, and 3D/4D content. Traditionally, generative models primarily focus on visual fidelity while often neglecting the physical plausibility of generated content. This gap limits their effectiveness in applications requiring adherence to real-world physical laws, such as robotics, autonomous systems, and scientific simulations. As generative AI evolves to increasingly integrate physical realism and dynamic simulation, its potential to function as a "world simulator" expands—enabling the modeling of interactions governed by physics and bridging the divide between virtual and physical realities. This survey systematically reviews this emerging field of physics-aware generative AI in computer vision, categorizing methods based on how they incorporate physical knowledge—either through explicit simulation or implicit learning. We analyze key paradigms, discuss evaluation protocols, and identify future research directions. By offering a comprehensive overview, this survey aims to help future developments in physically grounded generation for vision. The reviewed papers are summarized at https://***/BestJunYu/Awesome-physics-aware-Generation. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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A Complex Hermitian Positive Definite Manifold Embedding Transformer Network for Time-Varying Direction of Arrival Tracking

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Teng, Liping Wang, Qing Wang, Yanhui Guo, Qinghua Li, Wanqing Tianjin University School of Electrical and Information Engineering Tianjin300072 China Nantong University School of Information Science and Technology Nantong226019 China Science and Technology on Space Physics Laboratory Beijing100076 China University of Wollongong School of Electrical Computer and Telecommunications Engineering WollongongNSW2522 Australia University of Wollongong Advanced Multimedia Research Lab WollongongNSW2522 Australia

In this work, we address the challenge of stable and robust time-varying direction-of-arrival (DOA) tracking, where both the DOA trajectory and the number of sources change over time across different scenarios. We formulate the problem as a multi-label classification task and propose a novel solution leveraging Riemannian manifold geometry and advanced neural networks for sequential data processing. Specifically, we introduce the Complex Hermitian Positive Definite embedding Transformer (C-HPD-T) network, which learns statistical representations of covariance matrices. The C-HPD-T consists of an HPD module and a Transformer module. The HPD module learns an appropriate statistical representation and the complex Transformer module models the data temporally. In particular, in the C-HPD-T network, we build a complex singular value decomposition (SVD) module to ensure that complex structure is preserved during the calculation of each layer. Meanwhile, we design a Transformer-based sequential network to capture the temporal relationship of signals and for the time-varying DOAs. Through training the network with varying numbers of moving targets featuring diverse motion trajectories, the network demonstrates excellent performance and robustness against array imperfections. Extensive simulation results demonstrate its superior performance gains compared to existing methods. © 2015 IEEE.

关键词： Covariance matrix

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