论文标题
非线性动力学系统的数字双胞胎
Digital twins of nonlinear dynamical systems
论文作者
论文摘要
我们阐明了基于机器学习的数字双胞胎的设计命令，用于非线性动力学系统，但可以通过外部驾驶，该系统可用于监视目标系统的``健康''并预期其未来的崩溃。我们证明，通过单个或并行的储层计算配置，数字双胞胎能够挑战预测和监视任务。 Employing prototypical systems from climate, optics and ecology, we show that the digital twins can extrapolate the dynamics of the target system to certain parameter regimes never experienced before, make continual forecasting/monitoring with sparse real-time updates under non-stationary external driving, infer hidden variables and accurately predict their dynamical evolution, adapt to different forms of external driving, and extrapolate the global bifurcation behaviors to systems of some different sizes.这些功能使我们的数字双胞胎在重要的应用中吸引人，例如监视关键系统的健康状况并预测其由环境变化引起的潜在崩溃。
We articulate the design imperatives for machine-learning based digital twins for nonlinear dynamical systems subject to external driving, which can be used to monitor the ``health'' of the target system and anticipate its future collapse. We demonstrate that, with single or parallel reservoir computing configurations, the digital twins are capable of challenging forecasting and monitoring tasks. Employing prototypical systems from climate, optics and ecology, we show that the digital twins can extrapolate the dynamics of the target system to certain parameter regimes never experienced before, make continual forecasting/monitoring with sparse real-time updates under non-stationary external driving, infer hidden variables and accurately predict their dynamical evolution, adapt to different forms of external driving, and extrapolate the global bifurcation behaviors to systems of some different sizes. These features make our digital twins appealing in significant applications such as monitoring the health of critical systems and forecasting their potential collapse induced by environmental changes.
