Chaos最新文献_第7页

Remote training of a reservoir computer via digital twins. 通过数字双胞胎对水库计算机进行远程训练。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0273463

Yutaro Sekiguchi, Rie Sai, André Röhm, Takatomo Mihana, Tomoki Yamagami, Kazutaka Kanno, Atsushi Uchida, Ryoichi Horisaki

{"title":"Remote training of a reservoir computer via digital twins.","authors":"Yutaro Sekiguchi, Rie Sai, André Röhm, Takatomo Mihana, Tomoki Yamagami, Kazutaka Kanno, Atsushi Uchida, Ryoichi Horisaki","doi":"10.1063/5.0273463","DOIUrl":"https://doi.org/10.1063/5.0273463","url":null,"abstract":"The increasing energy consumption required for information processing has become a significant challenge, leading to growing interest in optical and optoelectronic reservoir computing as a more efficient alternative. Trained reservoir computers are especially suited for low-energy applications near the edge. However, the computational cost of training the reservoir output weights, particularly due to matrix operations, adds potentially unwanted complexity to the architecture. To lift this restriction, we propose a remote training approach using digital twins-virtual models that replicate the behavior of a physical reservoir. In particular, unlike traditional training methods, we do not need to record the reservoir states experimentally for every new task. This allows the physical reservoir to be used continuously for inference without interruptions. We constructed two types of digital twins: a differential equation-based model and a deep neural network (DNN) model. Using the proposed remote training on real experimental data for the Santa-Fe laser time-series task confirmed that both models successfully captured the dynamics of the optoelectronic reservoir, allowing accurate predictions and the export of weights from the digital twin to the real world. The equation-based model achieved higher prediction accuracy than the DNN model, while the DNN model demonstrated greater robustness to variations in hyperparameters. These results demonstrate that digital twins can effectively enable the remote training of reservoir computing systems.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144991606","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

Stability analysis and control of multi-vibro-impact capsule systems with distributed interactions. 具有分布相互作用的多振冲胶囊系统稳定性分析与控制。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0265258

Ruohan Mi, Jinwei Yu

引用次数: 0

A Mexican hat dance: Clustering in spatially non-exclusive particle systems. 墨西哥帽舞：空间非排他性粒子系统中的聚类。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0271815

D Sabin-Miller, D M Abrams

引用次数: 0

MEP-Net: Generating solutions to scientific problems with limited knowledge by maximum entropy principle. MEP-Net：利用最大熵原理在有限的知识下生成科学问题的解决方案。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0261211

Wuyue Yang, Liangrong Peng, Guojie Li, Liu Hong

引用次数: 0

Minimal quantum reservoirs with Hamiltonian encoding. 最小量子库与哈密顿编码。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0282921

Gerard McCaul, Juan Sebastian Totero Gongora, Wendy Otieno, Sergey Savel'ev, Alexandre Zagoskin, Alexander G Balanov

引用次数: 0

Dynamics-informed reservoir computing with visibility graphs. 动态通知油藏计算与可见性图。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0293030

Charlotte Geier, Rasha Shanaz, Merten Stender

{"title":"Dynamics-informed reservoir computing with visibility graphs.","authors":"Charlotte Geier, Rasha Shanaz, Merten Stender","doi":"10.1063/5.0293030","DOIUrl":"https://doi.org/10.1063/5.0293030","url":null,"abstract":"Accurate prediction of complex and nonlinear time series remains a challenging problem across engineering and scientific disciplines. Reservoir computing (RC) offers a computationally efficient alternative to traditional deep learning by training only the readout layer while employing a randomly structured and fixed reservoir network. Despite its advantages, the largely random reservoir graph architecture often results in suboptimal and oversized networks with poorly understood dynamics. Addressing this issue, we propose a novel Dynamics-Informed Reservoir Computing (DyRC) framework that systematically infers the reservoir network structure directly from the input training sequence. This work proposes to employ the visibility graph (VG) technique, which converts time series data into networks by representing measurement points as nodes linked by mutual visibility. The reservoir network is constructed by directly adopting the VG network from a training data sequence, leveraging the parameter-free visibility graph approach to avoid expensive hyperparameter tuning. This process results in a reservoir that is directly informed by the specific dynamics of the prediction task under study. We assess the DyRC-VG method through prediction tasks involving the canonical nonlinear Duffing oscillator, evaluating prediction accuracy and consistency. Compared to an Erdős-Rényi (ER) graph of the same size, spectral radius, and fixed density, we observe higher prediction quality and more consistent performance over repeated implementations in the DyRC-VG. An ER graph with density matched to the DyRC-VG can in some conditions outperform both approaches.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145079614","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

Energy-induced chimera-like states in bilayer memristive FitzHugh-Nagumo neural networks. 双层记忆性FitzHugh-Nagumo神经网络中能量诱导的类嵌合体状态。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0285156

Ying Xie, Xuening Li, Xueqin Wang, Zhiqiu Ye, Lijian Yang, Ya Jia

{"title":"Energy-induced chimera-like states in bilayer memristive FitzHugh-Nagumo neural networks.","authors":"Ying Xie, Xuening Li, Xueqin Wang, Zhiqiu Ye, Lijian Yang, Ya Jia","doi":"10.1063/5.0285156","DOIUrl":"https://doi.org/10.1063/5.0285156","url":null,"abstract":"Despite extensive efforts to analyze synchronization and chimera states, it is limited to understand their emergence from an energy-based perspective in multilayer network synchronization. In this study, the bilayer FitzHugh-Nagumo neural network is constructed and the heterogeneity is realized by distinct dynamics of periodic and chaotic firing patterns. By analyzing the energy patterns of neurons, it is discovered that the intralayer synchronization is independent of the interlayer coupling in networks. Under specific conditions of intralayer coupling strength and nearest-neighbor connectivity, periodic neurons with a small energy difference give rise to chimera-like states. Meanwhile, chaotic neurons with a large energy difference induce a traveling phase-wave pattern. Furthermore, nonlocal coupling with proper synaptic strength leads to the emergence of a strong chimera-like state, which maintains energy between the energies of synchronized and desynchronized cases. The results uncover an energy-driven mechanism underlying the emergence of complex collective behaviors in multilayer neuronal systems, and it offers potential guidance for designing energy-efficient neuromorphic circuits.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145085282","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

Quantization induced memory-nonlinearity transfer: Implications of analog-to-digital conversion in reservoir computing. 量化诱导的记忆非线性转移：油藏计算中模数转换的含义。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0273403

Max Austin, Kohei Nakajima

{"title":"Quantization induced memory-nonlinearity transfer: Implications of analog-to-digital conversion in reservoir computing.","authors":"Max Austin, Kohei Nakajima","doi":"10.1063/5.0273403","DOIUrl":"10.1063/5.0273403","url":null,"abstract":"The output-side behaviors of typical digital computing systems, such as simulated neural networks, are generally unaffected by the act of observation; however, this is not the case for the burgeoning field of physical reservoir computers (PRCs). Observer dynamics can limit or modify the natural state information of a PRC in many ways, and among the most common is the conversion from analog to digital data needed for calculations. Here, to aid in the development of novel PRCs, we investigate the effects of bounded, quantized observations on systems' natural computational abilities. By utilizing a classical reservoir computing (RC) (an echo-state network) and some PRCs (a pneumatic artificial muscle and a soft tentacle), we show that observed state quantization effectively converts a system's natural memory into higher-order, nonlinear dynamics. Furthermore, this same effect can assist in reducing detectable system errors in the presence of noise. We demonstrate how these effects, imposed only through output-end observations, can improve timer task robustness, target different computational task types, and even encode the chaotic dynamics of a Lorenz attractor in a simple linear RC in a closed loop.","PeriodicalId":9974,"journal":{"name":"Chaos","volume":"35 9","pages":""},"PeriodicalIF":3.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144999707","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

Dissipative solitons onset through modulational instability of the cubic complex Ginzburg-Landau equation with nonlinear gradients. 具有非线性梯度的三次复金兹堡-朗道方程的调制不稳定性引起耗散孤子。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0278588

M I Carvalho, M Facão, Orazio Descalzi

引用次数: 0

Minimal deterministic echo state networks outperform random reservoirs in learning chaotic dynamics. 最小确定性回声状态网络在学习混沌动力学方面优于随机储层。

IF 3.2 2区数学

Chaos Pub Date : 2025-09-01 DOI: 10.1063/5.0288751

F Martinuzzi

引用次数: 0