Forcing-informed resolvent analysis extracts data-consistent forcing and response modes for self-sustained flows by estimating input-output subspaces from nonlinear forcing snapshots.
On dynamic mode decomposition: Theory and applications
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A spectral mPOD reformulation uses compact disjoint frequency masks to block-diagonalize the correlation operator, shrinking eigenvalue problems to per-band sizes while recovering identical modes and singular values.
Dynamic Mode Decomposition shows that short contiguous spans of Vision Transformer blocks can be approximated by a low-rank linear operator K with high predictive fidelity for p<=4 steps, but this approximation fails to outperform an identity baseline when propagated to the final layer.
A residual from Hankel DMD on Wasserstein-mapped training distributions localizes grokking transitions in modular-addition Transformers with AUROC 0.93 and can precede onset under a sustained-threshold rule.
WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.
citing papers explorer
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Forcing-informed resolvent analysis: Identification of input-output relations in self-sustained flows
Forcing-informed resolvent analysis extracts data-consistent forcing and response modes for self-sustained flows by estimating input-output subspaces from nonlinear forcing snapshots.
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A Fast Spectral Formulation of the Multiscale Proper Orthogonal Decomposition
A spectral mPOD reformulation uses compact disjoint frequency masks to block-diagonalize the correlation operator, shrinking eigenvalue problems to per-band sizes while recovering identical modes and singular values.
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Dynamic Mode Decomposition along Depth in Vision Transformers
Dynamic Mode Decomposition shows that short contiguous spans of Vision Transformer blocks can be approximated by a low-rank linear operator K with high predictive fidelity for p<=4 steps, but this approximation fails to outperform an identity baseline when propagated to the final layer.
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Distributional Spectral Diagnostics for Localizing Grokking Transitions
A residual from Hankel DMD on Wasserstein-mapped training distributions localizes grokking transitions in modular-addition Transformers with AUROC 0.93 and can precede onset under a sustained-threshold rule.
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WSINDy for Model Predictive Control with Applications to Fusion, Drones, and Chaos
WSINDYc-MPC identifies governing dynamics more robustly than benchmarks under high noise, enabling longer prediction horizons and lower tracking errors in fusion, drone, chaos, and aircraft control tasks.