Transformers fail to predict catastrophic collapse in unseen parameter regimes of nonlinear dynamical systems, while reservoir computing reliably succeeds.
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Optimizing reservoir computing by minimizing error in the reconstructed invariant distribution reproduces Lyapunov exponents and chaotic attractors more reliably than maximizing prediction time.
ITF inflates curvature in switching AL-RNNs by conditioning on one regime path while marginal likelihood reduces curvature with a missing-information correction for plausible switches, and evidence fine-tuning can degrade dynamical QoIs despite better held-out evidence.
citing papers explorer
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Can Transformers predict system collapse in dynamical systems?
Transformers fail to predict catastrophic collapse in unseen parameter regimes of nonlinear dynamical systems, while reservoir computing reliably succeeds.
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Optimizing Reservoir Computing for Reconstructing Ergodic Properties
Optimizing reservoir computing by minimizing error in the reconstructed invariant distribution reproduces Lyapunov exponents and chaotic attractors more reliably than maximizing prediction time.
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Teacher Forcing as Generalized Bayes: Optimization Geometry Mismatch in Switching Surrogates for Chaotic Dynamics
ITF inflates curvature in switching AL-RNNs by conditioning on one regime path while marginal likelihood reduces curvature with a missing-information correction for plausible switches, and evidence fine-tuning can degrade dynamical QoIs despite better held-out evidence.