NeurIPS paper: predicting dynamics beyond tipping points without knowing control parameters
DangerousFunny1371 · reddit · 2026-10-02
The authors present their NeurIPS 2026 paper "Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction" (arXiv:2606.22969), tackling the hardest form of generalization in dynamical systems reconstruction (DSR) and time series forecasting: topological OOD generalization (OODG), where the dynamical regime itself changes (e.g., cyclic to chaotic).
- Such shifts occur when a system crosses bifurcations/tipping points driven by slowly varying control parameters: climate tipping, the brain tipping into epileptic activity, or a patient developing sepsis.
- Current SOTA TSF models rely on statistical regularities and cannot predict novel, unseen regimes; the control parameters driving regime changes are often unknown.
- The paper mathematically identifies key failure modes in prior hierarchical DSR models: they cannot correctly learn control parameters or extrapolate them beyond the training domain.
- Fixing these via feature-splitting and physical sparsity priors, the modified hierarchical DSR model correctly predicts bifurcations and beyond-bifurcation dynamics without any explicit knowledge of control parameters in training.
- The approach is generic, validated on shallow PLRNNs and Neural ODEs across discrete- and continuous-time RNNs.
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