IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0268836

Siyuan Chang, Wei Ma, Min Ye, Joseph Páez Chávez, Yelin Li, Yuchuan Ma, Jiale Zhang

{"title":"Delayed feedback control and parameter continuation of multistability in a nonsmooth hydraulic rock drill model.","authors":"Siyuan Chang, Wei Ma, Min Ye, Joseph Páez Chávez, Yelin Li, Yuchuan Ma, Jiale Zhang","doi":"10.1063/5.0268836","DOIUrl":"https://doi.org/10.1063/5.0268836","url":null,"abstract":"In response to the complex multistable behavior observed in hydraulic rock drills during the drilling process, this study first establishes a four-degree-of-freedom physical model based on dry friction rock mechanics theory. The motion trajectory is classified into three states: non-viscous, impact viscous, and buffer viscous. Using the impact frequency ω as the bifurcation parameter, multistable attractors p0q1 and p1q2 are identified in the system when ω = 9. To control the multistability, a delayed feedback control method is applied, in which the infinite-dimensional delay differential equations are approximated by finite-dimensional ordinary differential equations. The reliability of this approximation is validated through a distance function. When the control gain K = 9 and the delay time τd = 0.35, both attractors p0q1 and p1q2 are successfully converted into a single p0q1 attractor. Next, the pseudo-arclength continuation method and Floquet theory are employed to investigate parameter continuation and parameter domains. The period-doubling bifurcation points PD1 and PD2 divide the parameter space of K and τd into three distinct regions. Crossing these regions induces a supercritical period-doubling bifurcation. For constant K, a smaller τd leads to an increased number of collisions and periodic motions in the system. Simulation results demonstrate that by tuning the delay parameters, the multistability during the drilling process can be effectively controlled, thereby enhancing drilling efficiency and stability. Finally, rock drilling experiments confirm the validity of the model and the presence of multistability. When drilling into rocks with high hardness and brittleness, multistable motions are more likely to occur.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal control for phase locking of synchronized oscillator populations via dynamical reduction techniques. 基于动态约简技术的同步振子群锁相优化控制。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0275374

Narumi Fujii, Hiroya Nakao

引用次数: 0

Approximating the bifurcation diagram of weakly and strongly coupled leading-following systems. 弱耦合和强耦合先导-跟随系统分岔图的近似。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0269773

S Sinet, R Bastiaansen, C Kuehn, A S von der Heydt, H A Dijkstra

引用次数: 0

First passage properties of d-dimensional finite combs with different growth modes. 不同生长方式的d维有限梳子的首通特性。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0273216

Yuan Zhu, Zhenhua Yuan, Junhao Peng

引用次数: 0

Impact of internal noise on convolutional neural networks. 内部噪声对卷积神经网络的影响。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0275670

I D Kolesnikov, N Semenova

引用次数: 0

Chaos, dynamic trapping, and transport of swimming microbes in a vortex chain flow. 漩涡链流动中游动微生物的混沌、动态捕获和运输。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0270869

Nghia Le, Thomas H Solomon

引用次数: 0

Graph neural network for prediction of phase-ordering kinetics. 相序动力学预测的图神经网络。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0273728

Vijay Yadav, Madhu Priya, Manish Dev Shrimali, Prabhat K Jaiswal

引用次数: 0

Simplified neurochaos learning architectures for data classification. 用于数据分类的简化神经混沌学习架构。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0263796

Akhila Henry, Rajan Sundaravaradhan, Nithin Nagaraj

{"title":"Simplified neurochaos learning architectures for data classification.","authors":"Akhila Henry, Rajan Sundaravaradhan, Nithin Nagaraj","doi":"10.1063/5.0263796","DOIUrl":"https://doi.org/10.1063/5.0263796","url":null,"abstract":"Developing machine learning algorithms that can classify datasets with higher accuracy and efficiency is crucial in practical applications. Neurochaos learning (NL) is a recently proposed algorithm that is inspired by the chaotic firing of neurons in the brain. NL has shown promise in recent times both in terms of classification accuracy and in the number of samples needed for training. In this study, we propose a novel simplification of the neurochaos learning algorithm by reducing the number of features needed for classification and also reducing the number of hyperparameters needed to be tuned. By using a single feature of the chaotic neural traces (orbit generated by chaotic map) of NL and by using only one hyperparameter, we demonstrate a significant boost in run time of the algorithm while retaining comparable classification accuracy. This single feature could either be the mean of the chaotic neural traces (Tracemean) or the Fluctuation Index (FI) of the chaotic neural traces. The classifier itself could either be a simple cosine similarity (Tracemean ChaosNet, FI ChaosNet) or any of the classical machine learning (ML) classifiers (Tracemean+ML, FI+ML). We compare the performance of these newly proposed simplified NL algorithms on ten publicly available datasets. The proposed simplified NL architectures in this study are able to efficiently classify datasets while taking much less run time. The fact that only a single hyperparameter needs to be tuned in both architectures (Tracemean ChaosNet and FI ChaosNet) makes them very attractive for practical applications with the ease of interpretability.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 6","pages":""},"PeriodicalIF":2.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144198357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spatial locking of chimera states to frequency heterogeneity in nonlocally coupled oscillators. 非局域耦合振荡器中嵌合体状态对频率异质性的空间锁定。

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0266425

Petar Mircheski, Hiroya Nakao

引用次数: 0

Imre M. Jánosi (1963-2023).

IF 2.7 2区数学

Chaos Pub Date : 2025-06-01 DOI: 10.1063/5.0274421

Marcus W Beims, Pedro G Lind, Thorsten Pöschel, Támas Tél, Miklós Vincze, Dietrich E Wolf

引用次数: 0

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