Eigenanalysis framework for autoregressive neural emulators of multi-scale chaotic dynamics
Neural autoregressive models have rapidly emerged as powerful emulators of high-dimensional chaotic systems, yet their long-term instability and error growth remain poorly understood, leading to ad-hoc solutions. Here, we develop an eigenanalysis framework that reveals the dynamical origin of this error growth. By analyzing the Jacobian of the learned one-step update map with respect to the state, we show how inference-time error growth, and thus model stability, is governed by its spectral radius. Direct-step architectures (models that predict the next state from the previous one) generically admit unstable eigenvalues with magnitudes exceeding one, explaining the rapid divergence of the...
| Authors | Conrad Ainslie, Pedram Hassanzadeh, Michael W. Mahoney et al. |
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| Primary category | cs.AI |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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