气候变化领域集成服务门户(CCPortal): Reservoir computing as digital twins for nonlinear dynamical systems

CCPortal

DOI	10.1063/5.0138661
	Reservoir computing as digital twins for nonlinear dynamical systems
	Kong, Ling-Wei; Weng, Yang; Glaz, Bryan; Haile, Mulugeta; Lai, Ying-Cheng
发表日期	2023
ISSN	1054-1500
EISSN	1089-7682
卷号	33 期号:3
英文摘要	We articulate the design imperatives for machine learning based digital twins for nonlinear dynamical systems, which can be used to monitor the health of the system and anticipate future collapse. The fundamental requirement for digital twins of nonlinear dynamical systems is dynamical evolution: the digital twin must be able to evolve its dynamical state at the present time to the next time step without further state input-a requirement that reservoir computing naturally meets. We conduct extensive tests using prototypical systems from optics, ecology, and climate, where the respective specific examples are a chaotic CO(2 )laser system, a model of phytoplankton subject to seasonality, and the Lorenz-96 climate network. We demonstrate that, with a single or parallel reservoir computer, the digital twins are capable of a variety of challenging forecasting and monitoring tasks. Our digital twin has the following capabilities: (1) extrapolating the dynamics of the target system to predict how it may respond to a changing dynamical environment, e.g., a driving signal that it has never experienced before, (2) making continual forecasting and monitoring with sparse real-time updates under non-stationary external driving, (3) inferring hidden variables in the target system and accurately reproducing/predicting their dynamical evolution, (4) adapting to external driving of different waveform, and (5) extrapolating the global bifurcation behaviors to network systems of different sizes. These features make our digital twins appealing in applications, such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes or perturbations. Such systems can be an infrastructure, an ecosystem, or a regional climate system.
语种	英语
WOS研究方向	Mathematics, Applied ; Physics, Mathematical
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000945977600004
来源期刊	CHAOS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281239
作者单位	Arizona State University; Arizona State University-Tempe; Arizona State University; Arizona State University-Tempe
推荐引用方式 GB/T 7714	Kong, Ling-Wei,Weng, Yang,Glaz, Bryan,et al. Reservoir computing as digital twins for nonlinear dynamical systems[J],2023,33(3).
APA	Kong, Ling-Wei,Weng, Yang,Glaz, Bryan,Haile, Mulugeta,&Lai, Ying-Cheng.(2023).Reservoir computing as digital twins for nonlinear dynamical systems.CHAOS,33(3).
MLA	Kong, Ling-Wei,et al."Reservoir computing as digital twins for nonlinear dynamical systems".CHAOS 33.3(2023).