2 papers
cs.LG2026
Parallel-in-Time Training of Recurrent Neural Networks for Dynamical Systems Reconstruction
Florian Hess, Florian Götz, Daniel Durstewitz
Reconstructing nonlinear dynamical systems (DS) from data (DSR) is a fundamental challenge in science and engineering, but it inherently relies on sequential models. Recent breakth…
cs.LG2026
Continuous-Time Piecewise-Linear Recurrent Neural Networks
Alena Brändle, Alena Brändle, Lukas Eisenmann +3
In dynamical systems reconstruction (DSR) we aim to recover the dynamical system (DS) underlying observed time series. Specifically, we aim to learn a generative surrogate model wh…