吴玥,颜子翔,高健,杨胡江,王新刚,肖井华

• 1:北京邮电大学物理科学与技术学院
• 2:陕西师范大学物理学与信息技术学院

摘要(Abstract)：

人工智能技术与大学物理实验的结合已成为物理教学发展的重要趋势，探索二者深度融合的路径对于实现数智化教学新模式具有重要意义。本研究聚焦于一种广泛应用于非线性实验教学的机器学习方法——储备池计算(Reservoir Computing, RC),探索其在耦合蔡氏电路实验教学中进行同步预测的应用。研究表明，利用储备池计算对耦合蔡氏电路三个非同步状态下的四路电压信号进行学习，可以实现达到同步状态所需电阻值的准确预测。这不仅为学生提供了一种便捷的同步调节方法，还使他们能够直观感受到人工智能技术在物理实验中的巨大应用潜力，从而丰富了大学物理实验教学的数智化内容。

关键词(KeyWords)： 耦合蔡氏电路实验;储备池计算;同步预测;参数感知

Abstract:

Keywords:

基金项目(Foundation): 2024高等学校计算物理课程教学研究项目(JZW-24-JW-06);; 2024年度全国高等学校大学物理改革研究项目(2024PR007);; 北京邮电大学研究生教改项目(2023Y032)

作者(Author): 吴玥,颜子翔,高健,杨胡江,王新刚,肖井华

参考文献(References)：

• [1] CUI Z,JING X,ZHAO P,et al.A new subspace clustering strategy for AI-based data analysis in IoT system[J].IEEE Internet of Things Journal,2021,8(16):12540-12549.
• [2] ZHANG L,ZHANG L.Artificial intelligence for remote sensing data analysis:A review of challenges and opportunities[J].IEEE Geoscience and Remote Sensing Magazine,2022,10(2):270-294.
• [3] SCHMIDT M,LIPSON H.Distilling free-form natural laws from experimental data[J].Science,2009,324(5923):81-85.
• [4] AL KUWAITI A,NAZER K,AL-REEDY A,et al.A review of the role of artificial intelligence in healthcare[J].Journal of Personalized Medicine,2023,13(6):951.
• [5] ALMASRI F.Exploring the impact of artificial intelligence in teaching and learning of science:A systematic review of empirical research[J].Research in Science Education,2024,54(5):977-997.
• [6] JAIN D.Artificial intelligence in quality control systems:a cross-industry analysis of applications,benefits and implementation frameworks[J].International Journal of Scientific Research in Computer Science,Engineering and Information Technology,2024,10(6):1321-1333.
• [7] MAHLIGAWATI F,ALLANAS E,BUTARBUTAR M H,et al.Artificial intelligence in Physics Education:a comprehensive literature review[C]//Journal of Physics:Conference Series.IOP Publishing,2023,2596(1):012080.
• [8] Veluru C S.Data Mining Best Practices and Efficiency in the Large-Scale Data Mining Using Artificial Intelligence and Generative AI[J].Journal of Artificial Intelligence & Cloud Computing,2024,300:2-4.
• [9] CHEN X,XIE H,ZOU D,et al.Application and theory gaps during the rise of artificial intelligence in education[J].Computers and Education:Artificial Intelligence,2020,1:100002.
• [10] 李海红，代琼琳，王世红，等.一种简单的基于蔡氏电路的数字加密通信实验[J].大学物理，2006,25(9):39-39.LI H H,DAI Q L,WANG S H,et al.A Simple Digital Secure Communication Experiment Based on Chua's Circuit[J].College Physics,2006,25(9):39-39.(in Chinese)
• [11] MAASS W,NATSCHL??GER T,MARKRAM H.Real-time computing without stable states:A new framework for neural computation based on perturbations[J].Neural Computation,2002,14(11):2531-2560.
• [12] JAAGER H,HAAS H.Harnessing nonlinearity:Predicting chaotic systems and saving energy in wireless communication[J].Science,2004,304(5667):78-80.
• [13] JAAGER H.The “echo state” approach to analysing and training recurrent neural networks-with an erratum note[R].Bonn,Germany:German National Research Center for Information Technology GMD Technical Report,2001,148(34):13.
• [14] MAASS W,NATSCHL??GER T,MARKRAM H.Real-time computing without stable states:A new framework for neural computation based on perturbations[J].Neural Computation,2002,14(11):2531-2560.
• [15] CUI H,XIAO Y,YANG Y,et al.A bioinspired in-materia analog photoelectronic reservoir computing for human action processing[J].Nature Communications,2025,16(1):2263.
• [16] BALDINI P.Reservoir computing in robotics:a review[J].arXiv preprint arXiv:2206.11222,2022.
• [17] KONG L W,FAN H W,GREBOGI C,et al.Machine learning prediction of critical transition and system collapse[J].Physical Review Research,2021,3(1):013090.
• [18] ROY M,MANDAL S,HENS C,et al.Model-free prediction of multistability using echo state network[J].Chaos:An Interdisciplinary Journal of Nonlinear Science,2022,32(10).
• [19] JAEGER H,HAAS H.Harnessing nonlinearity:Predicting chaotic systems and saving energy in wireless communication[J].science,2004,304(5667):78-80.
• [20] Luo H,Du Y,Fan H,et al.Reconstructing bifurcation diagrams of chaotic circuits with reservoir computing[J].Physical Review E,2024,109(2):024210.
• [21] 颜子翔，吴玥，谢桂今，等.基于储备池计算的混沌扭摆数字孪生系统[J].大学物理，2024,44(4):7-13.YAN Z X,WU Y,XIE G J,et al.A digital-twin system for chaotic torsion pendulum based on reservoir computing[J].College Physics,2024,44(4):7-13.(in Chinese)
• [22] 戴文娇，颜子翔，高健，等.储备池计算在非线性电路实验教学中的应用[J].物理与工程，2025,35(3):194-200.DAI W J,YAN Z X,GAO J,et al.Application of Reservoir Computing in Nonlinear Circuit Experiment Teaching[J].Physics and Engineering,2025,35(3):194-200.(in Chinese)
• [23] CHUA L O,KOCAREV L,ECKERT K,et al.Experimental chaos synchronization in Chua's circuit[J].International Journal of Bifurcation and Chaos,1992,2(3):705-708.
• [24] PECORA L M,CARROLL T L.Synchronization in chaotic systems[J].Physical review letters,1990,64(8):821.
• [25] NATIONAL INSTRUMENTS.LabVIEW入门指南[M].Texas,USA:National Instruments,2010.
• [26] 高健，颜子翔，肖井华.储备池算法与动力系统分析研究进展[J].北京师范大学学报(自然科学版),2023,59(6):860-868.GAO J,YAN Z X,XIAO J H.Research progress on reservoir algorithm and dynamical system analysis[J].Journal of Beijing Normal University (Natural Science Edition),2023,59(6):860-868.(in Chinese)
• [27] ZHANG H,FAN H,WANG L,et al.Learning Hamiltonian dynamics with reservoir computing[J].Physical Review E,2021,104(2):024205.
• [28] BISCHL B,BINDER M,LANG M,et al.Hyperparameter optimization:Foundations,algorithms,best practices,and open challenges[J].Wiley Interdisciplinary Reviews:Data Mining and Knowledge Discovery,2023,13(2):e1484.