Small transformers learn to forecast unseen dynamical systems in-context by using delay embeddings to recover the manifold and forecasting its invariant sets via a transfer-operator strategy.
A mechanistic analysis of transformers for dynamical systems
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A rank-optimized Koopman-Hankel digital twin enables equation-free, input-blind reconstruction of nonlinear structural dynamics with R² > 0.95 on an NREL 5MW floating wind turbine.
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Transformers for dynamical systems learn transfer operators in-context
Small transformers learn to forecast unseen dynamical systems in-context by using delay embeddings to recover the manifold and forecasting its invariant sets via a transfer-operator strategy.
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Equation-Free Digital Twins for Nonlinear Structural Dynamics
A rank-optimized Koopman-Hankel digital twin enables equation-free, input-blind reconstruction of nonlinear structural dynamics with R² > 0.95 on an NREL 5MW floating wind turbine.