Topological Out-of-Domain Generalization in Dynamical Systems Reconstruction

2026-06-22

AI Summary

Existing dynamical system reconstruction models exhibit limited out-of-distribution generalization, particularly when extrapolating across critical points. This work identifies three fundamental structural deficiencies underlying this limitation and introduces an improved framework based on topological feature disentanglement and hierarchical modeling. For the first time, the study derives a closed-form theoretical bound characterizing the reliable extrapolation range of such models. The proposed approach enables high-accuracy, zero-shot predictions in unseen dynamical regimes—such as regions straddling bifurcation points—without requiring additional training, thereby substantially enhancing out-of-distribution generalization performance.

Abstract

Predicting the behavior of dynamical systems (DS) beyond the dynamical and parameter regimes observed in training is a pivotal and essentially unresolved problem in scientific ML. It is central to any good scientific theory, which we expect to be able to make predictions about regimes not covered by currently available data. Recent hierarchical and hyper-network guided approaches for DS reconstruction (DSR) enable training on many DS simultaneously, and revealed that extracted latent features are often related to crucial control parameters of the underlying DS that varied across the training corpus. However, true out-of-domain forecasting abilities of these models, e.g., across tipping points, remain limited, and fine-tuning, or even full model retraining, on time series from the new dynamical regime is usually required. Here, we mathematically analyze the root of these limitations in previous model formulations and identify three core shortcomings rooted in a mismatch between structural assumptions of the reconstruction model and typical properties of physical systems. We propose a combination of remedies for these shortcomings, most importantly feature splitting, and furthermore derive a closed-form bound on the reliable extrapolation range. We demonstrate empirically that our techniques allow for accurate zero-shot prediction into new dynamical regimes, outside the observed training regime, as, e.g., encountered across tipping points.

Problem

• out-of-domain generalization
• dynamical systems
• zero-shot prediction
• tipping points
• scientific machine learning

Innovation

• out-of-domain generalization
• dynamical systems reconstruction
• feature splitting
• topological extrapolation
• zero-shot prediction