Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction [R]

A NeurIPS 2026 spotlight paper, "Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction," introduces a method to significantly accelerate the training of nonlinear RNNs on chaotic dynamical systems. By integrating DEER with generalized teacher forcing (GTF), the researchers achieved over 100x speedup. This approach enables efficient parallel-in-time and stable training on extremely long time series (T>10^6), outperforming models like Mamba in Dynamical Systems Reconstruction (DSR) tasks.