Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
This work addresses a specific issue in scientific computing for researchers simulating complex dynamical systems, though it appears incremental as it builds on existing neural operator and RNN methods.
The authors tackled the problem of error accumulation in long-time integration for dynamical systems using neural networks by combining neural operators with recurrent neural networks, resulting in stabilized solutions and reduced error accumulation for the Korteweg-de Vries equation.
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 dependencies. The integrated framework is shown to stabilize the solution and reduce error accumulation for both interpolation and extrapolation of the Korteweg-de Vries equation.