Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction [R]

A recent #NeurIPS2026 paper, "Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction," addresses a fundamental challenge in dynamical systems reconstruction (DSR) and time series forecasting (TSF). While many state-of-the-art models can generalize to new initial conditions or changing statistical properties, the more difficult problem is topological out-of-domain generalization (OODG). This occurs when the dynamical regime itself changes, for example, transitioning from cyclic to chaotic behavior.