Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks

arXiv:2303.02243v3 Announce Type: replace
Abstract: Deep neural networks are an attractive alternative for simulating complex dynamical systems, as in comparison to traditional scientific computing methods, they offer reduced computational costs during inference and can be trained directly from observational data. Existing methods, however, cannot extrapolate accurately and are prone to error accumulation in long-time integration. Herein, we address this issue by combining neural operators with recurrent neural networks, learning the operator mapping, while offering a recurrent structure to capture temporal …

abstract alternative arxiv comparison computational computing costs cs.lg data however inference integration networks neural networks recurrent neural networks scientific systems type