Transfer learning of chaotic systems
Can a neural network trained by the time series of system A be used to predict the evolution of system B? This problem, knowing as transfer learning in a broad sense, is of great importance in machine learning and data mining, yet has not been addressed for chaotic systems. Here we investigate transfer learning of chaotic systems from the perspective of synchronization-based state inference, in which a reservoir computer trained by chaotic system A is used to infer the unmeasured variables of chaotic system B, while A is different from B in either parameter or dynamics. It is found that if systems A and B are different in parameter, the reservoir computer can be well synchronized to system B. However, if systems A and B are different in dynamics, the reservoir computer fails to synchronize with system B in general. Knowledge transfer along a chain of coupled reservoir computers is also studied, and it is found that, although the reservoir computers are trained by different systems, the unmeasured variables of the driving system can be successfully inferred by the remote reservoir computer. Finally, by an experiment of chaotic pendulum, we show that the knowledge learned from the modeling system can be used to predict the evolution of the experimental system.
Code (0)
등록된 구현이 없습니다.
Tasks
General KnowledgeTime SeriesTime Series AnalysisTransfer LearningSimilar Papers 제목 키워드 기반
Attractor learning for spatiotemporally chaotic dynamical systems using echo state networks with transfer learning
In this paper, we explore the predictive capabilities of echo state networks (ESNs) for the generalized Kuramoto-Sivashinsky (gKS) equation, an archetypal nonlinear PDE that exhibits spatiotemporal chaos. We introduce a …
Transfer LearningKnowledge-based Deep Learning for Modeling Chaotic Systems
Deep Learning has received increased attention due to its unbeatable success in many fields, such as computer vision, natural language processing, recommendation systems, and most recently in simulating multiphysics prob…
Deep LearningRecommendation SystemsChaos as an interpretable benchmark for forecasting and data-driven modelling
The striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, chaotic systems can be repeatedly measured to produce arbitrarily-detailed information about…
BenchmarkingSymbolic RegressionTime SeriesTime Series Analysis+2Cause-Effect Preservation and Classification using Neurochaos Learning
Discovering cause-effect from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algorithm namely-\emph{Neurochaos Learning} (…
ClassificationTime SeriesTime Series AnalysisTransfer LearningAnalysis of chaotic dynamical systems with autoencoders
We focus on chaotic dynamical systems and analyze their time series with the use of autoencoders, i.e., configurations of neural networks that map identical output to input. This analysis results in the determination of …
Time SeriesTime Series Analysis