RefreshNet: Learning Multiscale Dynamics through Hierarchical Refreshing
Junaid Farooq Danish Rafiq Pantelis R. Vlachas Mohammad Abid Bazaz
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.