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arXiv 2025年

作者： Hirschman, Jack Abedi, Erfan Wang, Minyang Zhang, Hao Borthakur, Abhimanyu Baker, Justin Carbajo, Sergio Department of Applied Physics Stanford University StanfordCA94305 United States SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Department of Electrical & Computer Engineering UCLA Los AngelesCA90095 United States Department of Mathematics UCLA Los AngelesCA90095 United States Department of Statistics & Data Science UCLA Los AngelesCA90095 United States Physics & Astronomy Department UCLA Los AngelesCA90095 United States California NanoSystems Institute Los AngelesCA90095 United States

Modeling second-order (χ(2)) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods like the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback and control. We present an LSTM-based surrogate model trained on SSFM simulations generated from a start-to-end model of the photocathode drive laser at SLAC National Accelerator laboratory’s Linac Coherent Light Source II. The model achieves over 250× speedup while maintaining high fidelity, enabling future real-time optimization and laying the foundation for data-integrated modeling frameworks. © 2025, CC BY.

关键词： Pulse repetition rate

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Anderson Acceleration in Nonsmooth Problems: Local Convergence via Active Manifold Identification

arXiv

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arXiv 2024年

作者： Li, Kexin Bai, Luwei Wang, Xiao Wang, Hao School of Information Science and Technology ShanghaiTech University Shanghai China Department of AI Computing Peng Cheng Laboratory Shenzhen China

Anderson acceleration is an effective technique for enhancing the efficiency of fixed-point iterations;however, analyzing its convergence in nonsmooth settings presents significant challenges. In this paper, we investigate a class of nonsmooth optimization algorithms characterized by the active manifold identification property. This class includes a diverse array of methods such as the proximal point method, proximal gradient method, proximal linear method, proximal coordinate descent method, Douglas-Rachford splitting (or the alternating direction method of multipliers), and the iteratively reweighted 1 method, among others. Under the assumption that the optimization problem possesses an active manifold at a stationary point, we establish a local R-linear convergence rate for the Anderson-accelerated algorithm. Our extensive numerical experiments further highlight the robust performance of the proposed Anderson-accelerated methods. Copyright © 2024, The Authors. All rights reserved.

关键词： optimization algorithms

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DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical Representations

arXiv

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arXiv 2024年

作者： Zhu, Guogang Liu, Xuefeng Niu, Jianwei Tang, Shaojie Wu, Xinghao Zhang, Jiayuan State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Department of Management Science and Systems University at Buffalo BuffaloNY United States

In personalized federated learning (PFL), it is widely recognized that achieving both high model generalization and effective personalization poses a significant challenge due to their conflicting nature. As a result, existing PFL methods can only manage a trade-off between these two objectives. This raises an interesting question: Is it feasible to develop a model capable of achieving both objectives simultaneously? Our paper presents an affirmative answer, and the key lies in the observation that deep models inherently exhibit hierarchical architectures, which produce representations with various levels of generalization and personalization at different stages. A straightforward approach stemming from this observation is to select multiple representations from these layers and combine them to concurrently achieve generalization and personalization. However, the number of candidate representations is commonly huge, which makes this method infeasible due to high computational costs. To address this problem, we propose DualFed, a new method that can directly yield dual representations correspond to generalization and personalization respectively, thereby simplifying the optimization task. Specifically, DualFed inserts a personalized projection network between the encoder and classifier. The pre-projection representations are able to capture generalized information shareable across clients, and the post-projection representations are effective to capture task-specific information on local clients. This design minimizes the mutual interference between generalization and personalization, thereby achieving a win-win situation. Extensive experiments show that DualFed can outperform other FL methods. Code is available at https://***/GuogangZhu/DualFed. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Single Objective optimization of Heliostat Field Efficiency in Tower Power Plants Based on Monte Carlo Algorithm

Single Objective Optimization of Heliostat Field Efficiency ...

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Computers, Information Processing and Advanced Education (CIPAE), 2020 International Conference on

作者： Shao Wenzhen International Joint Laboratory for Integrated Circuits Design and Application Ministry of Education School of Physics Zhengzhou University Zhengzhou China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

This paper established a single objective nonlinear tower heliostat field optimization design model based on the principle of solar rays entering and exiting, coordinate transformation, etc. It uses traversal algorithm, improved Monte Carlo algorithm, improved particle swarm optimization algorithm, etc. to solve the problem, and finally obtains the relevant data parameters of the heliostat field. The paper introduces a shadow occlusion judgment matrix, which projects related heliostats onto the ground plane. Monte Carlo algorithm is used to scatter points onto the ground plane, eliminating random points affected by occlusion and shadow judgment matrix. This is used to calculate the efficiency of shadow occlusion, which has great reference value for designing and controlling heliostat fields.

关键词： Monte Carlo methods Poles and towers Layout Mathematical models Software Mirrors Optical scattering Particle swarm optimization optimization Power generation

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Near-optimal dynamic regret for adversarial linear mixture MDPs 24

Near-optimal dynamic regret for adversarial linear mixture M...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Long-Fei Li Peng Zhao Zhi-Hua Zhou National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China

ISBN: (纸本)9798331314385

We study episodic linear mixture MDPs with the unknown transition and adversarial rewards under full-information feedback, employing dynamic regret as the performance measure. We start with in-depth analyses of the strengths and limitations of the two most popular methods: occupancy-measure-based and policy-based methods. We observe that while the occupancy-measure-based method is effective in addressing non-stationary environments, it encounters difficulties with the unknown transition. In contrast, the policy-based method can deal with the unknown transition effectively but faces challenges in handling non-stationary environments. Building on this, we propose a novel algorithm that combines the benefits of both methods. Specifically, it employs (i) an occupancy-measure-based global optimization with a two-layer structure to handle non-stationary environments; and (ii) a policy-based variance-aware value-targeted regression to tackle the unknown transition. We bridge these two parts by a novel conversion. Our algorithm enjoys an $\widetilde{\mathcal{O}}(d \sqrt{H^3 K} + \sqrt{HK(H + \bar{P}_K)})$ dynamic regret, where d is the feature dimension, H is the episode length, K is the number of episodes, $\bar{P}_K$ is the non-stationarity measure. We show it is minimax optimal up to logarithmic factors by establishing a matching lower bound. To the best of our knowledge, this is the first work that achieves near-optimal dynamic regret for adversarial linear mixture MDPs with the unknown transition without prior knowledge of the non-stationarity measure.

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Detection and Analyze of Off-Maximum Power Points of PV systems Based on PV-Pro modelling

Detection and Analyze of Off-Maximum Power Points of PV Syst...

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IEEE Conference Photovoltaic Specialists (PVSC)

作者： Baojie Li Xin Chen Anubhav Jain Energy Technologies Area Lawrence Berkeley National Laboratory Berkeley CA USA

Photovoltaic (PV) systems can operate off the maximum power point (MPP) for various reasons. Understanding when off- MPP behavior occurs is essential to the maintenance and operation (O&M) of PV systems. To detect off-MPP data, a reference power is usually needed, which can be obtained by system modeling that generally relies on physical model parameters. Traditional methods commonly obtain these parameters based on the initial condition of the PV system such as from the module datasheet. However, these parameters often do not reflect the current condition of the on-site PV system, which is likely to suffer from degradation and faults after years of operation with degraded parameters. Thus, we propose an off-MPP analysis algorithm based on the PV-Pro method, which can extract the model parameters (like series and shunt resistance) at the current operating condition only using the routine production data. In this way, the system power, current, and voltage can be accurately modeled. The off-MPP points are detected by comparing the measured power with the one modeled by PV-Pro. Points with large disagreement in power are further analyzed by deconvolving it into the error of the current and voltage at MPP, which allows tracing the error source of the off- MPP and provides valuable information for the O&M of PV systems. This off-MPP analysis is demonstrated on a 271kW PV field system, where it is shown that most of the off- MPP points are caused by the reduced DC current.

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The Causes of Errors in Accounting for Electric Energy of Agricultural Consumers

The Causes of Errors in Accounting for Electric Energy of Ag...

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International Conference on Control systems, Mathematical modelling, Automation and Energy Efficiency (SUMMA)

作者： Sergey Kirillov Alexander Vinogradov Michurinsk power supply division of the Open Joint Stock Company “Russian Railways” Federal State Budgetary Educational Institution of Higher Education Michurinsk State Agrarian University Michurinsk Russia Laboratory of Electricity Supply Electrical Equipment and Renewable Energy Federal Scientific Agroengineering Center VIM Moscow Russia Department of Electricity Supply Oryol State Agrarian University Oryol Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

The reasons for the occurrence of errors in the accounting of electric energy are given. A number of situations are considered, defined as unauthorized impact on the accounting of electric energy and increased error of devices and complexes for accounting of electric energy. The measures for organizing the system of control over electricity consumption at all levels of power supply are considered. methods for reducing under-accounting and errors in accounting of electricity are proposed.

关键词： Voltage measurement Power measurement Power supplies Electricity Measurement uncertainty Energy measurement Transformers Loss measurement Software measurement Monitoring

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The Multi-domain Modeling and optimization of Aircraft Electrical System Based on Modelica

The Multi-domain Modeling and Optimization of Aircraft Elect...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Jin Xiao Yue Li Xiaoguang Hu State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China

Aircraft electrical system includes key equipment such as battery, step-down circuit, filter, motor and so on. It is a comprehensive system integrating machinery, electronics, control, thermodynamics and other fields. However, many current modeling and simulation methods are aimed at specific problems in a single domain or subsystem, and it is difficult to consider the coupling relationship between different domains. To solve the above problems, this paper uses the multi-domain physical system modeling language Modelica to establish the aircraft electrical system model library on the simulation platform Mworks, and simulates the electrical characteristics of each model, some parameters are optimized. The results show that the multi-domain modeling of aircraft electrical system based on Modelica language can realize the simulation results close to the actual, and the optimization of related parameters can effectively improve the system performance and improve the design efficiency.

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A Novel Ins/Usbl/Dvl Integrated Navigation Scheme Against complex Underwater Environment

SSRN

引用

SSRN 2023年

作者： Qin, Hongmao Wang, Xi Wang, Guangcai Hu, Manjiang Bian, Yougang Qin, Xiaohui Ding, Rongjun State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body College of Mechanical and Vehicle Engineering Hunan University Changsha410082 China Wuxi Intelligent Control Research Institute of Hunan University Wuxi214115 China

In view of challenges of complex underwater environments with a high occurrence of sensor outliers, traditional Kalman-filter-based localization methods are difficult to achieve precise positioning. To address this issue, a factor graph optimization (FGO)-based tightly-coupled INS/USBL/DVL scheme and core fusion algorithm are proposed. The FGO method is used to construct a tightly-coupled INS/USBL/DVL factor graph model, and a weight function is added to adjust the weights of each factor, thereby improving the utilization of underwater sensor information and enhancing the accuracy and reliability of underwater navigation. The feasibility of the proposed scheme is verified through simulations and field test. The results show that the proposed method outperforms KF-LC, KF-TC, and FGO-LC methods, with horizontal position accuracy improvements of 29.2%, 6.6%, and 13.2%, respectively. Moreover, the proposed method exhibits smooth navigation error performance in the presence of abnormal sensor information. © 2023, The Authors. All rights reserved.

关键词： Navigation

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Artificial Intelligence methods Applied To Wind And Solar Energy Forecasting: A Comparative Study of Current Techniques

Artificial Intelligence Methods Applied To Wind And Solar En...

引用

Digital Age & Technological Advances for Sustainable Development (ICDATA), International Conference on

作者： Bouabdallaoui Doha Haidi Touria Derri Mounir Elmariami Faissal Tarraq Ali Electrical engineering department Laboratory of Systems Engineering Ecole Hassania des Travaux Publics Casablanca Morocco Electrical engineering department Laboratory of Electric Systems and Energy Ecole Nationale Supérieure d'Electricité et de Mécanique Casablanca Morocco

Given the scarcity of fossil sources, the necessity of switching to renewable sources is increasing. For that, the world recognizes the rising use of these renewable energies, especially the number of wind and solar installations which is constantly growing. In order to take advantage of these sources and implement them properly, the subject of forecasting the energy produced from these renewable sources is gaining more and more importance and interest. Several methods are proposed for forecasting the renewable energy production. Artificial intelligence methods are the widely used methods. This article presents a synthesis of machine learning techniques employed for predicting solar and wind power. First, artificial neural network approach and Support Vector Regression model applied to predict photovoltaic power have been detailed by discussing the selected inputs and the criteria adopted to evaluate their performance. Secondly, artificial neural networks model (ANN) and Support Vector Machine model (SVM) employed for the prediction of wind power have been discussed, including their configuration and their use frequency. The proposed methods have good performance. However, in order to improve their accuracy and minimize errors, combining several methods of artificial intelligence and optimization algorithms is the most suitable proposition.

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