Forecasting complex system dynamics, particularly for long-term predictions, is persistently hindered by error accumulation and computational burdens. This study presents RefreshNet, a multiscale framework developed to overcome these challenges, delivering an unprecedented balance between computational efficiency and predictive accuracy. RefreshNet incorporates convolutional autoencoders to identify a reduced order latent space capturing essential features of the dynamics, and strategically employs multiple recurrent neural network (RNN) blocks operating at varying temporal resolutions within the latent space, thus allowing the capture of latent dynamics at multiple temporal scales. The unique "refreshing" mechanism in RefreshNet allows coarser blocks to reset inputs of finer blocks, effectively controlling and alleviating error accumulation. This design demonstrates superiority over existing techniques regarding computational efficiency and predictive accuracy, especially in long-term forecasting. The framework is validated using three benchmark applications: the FitzHugh-Nagumo system, the Reaction-Diffusion equation, and Kuramoto-Sivashinsky dynamics. RefreshNet significantly outperforms state-of-the-art methods in long-term forecasting accuracy and speed, marking a significant advancement in modeling complex systems and opening new avenues in understanding and predicting their behavior.

翻译：预测复杂系统动力学，尤其是长期预测，始终受到误差累积和计算负担的阻碍。本研究提出RefreshNet——一种旨在克服这些挑战的多尺度框架，在计算效率与预测精度之间实现了前所未有的平衡。RefreshNet采用卷积自编码器识别出能够捕捉动力学核心特征的降阶潜空间，并策略性地在潜空间内部署多个以不同时间分辨率运行的循环神经网络模块，从而实现对多时间尺度潜动力学的捕获。RefreshNet独特的"刷新"机制允许粗粒度模块重置细粒度模块的输入，进而有效控制并缓解误差累积。这一设计在计算效率与预测精度方面（尤其是长期预测）展现出优于现有技术的性能。该框架通过三个基准应用进行验证：FitzHugh-Nagumo系统、反应扩散方程和Kuramoto-Sivashinsky动力学。RefreshNet在长期预测的精度与速度上显著超越现有最优方法，标志着复杂系统建模的重大进展，并为理解与预测其行为开辟了新途径。