Pith. sign in

REVIEW 3 cited by

Bridging the Gap between Koopmanism and Response Theory: Using Natural Variability to Predict Forced Response

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2410.01622 v2 pith:SOQXYET6 submitted 2024-10-02 cond-mat.stat-mech nlin.CD

classification cond-mat.stat-mechnlin.CD
keywords responsesystemnaturalvariabilityassociatedforcedfunctionskoopmanism
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The fluctuation-dissipation theorem is a cornerstone result in statistical mechanics that can be used to translate the statistics of the free natural variability of a system into information on its forced response to perturbations. By combining this viewpoint on response theory with the key ingredients of Koopmanism, it is possible to deconstruct virtually any response operator into a sum of terms, each associated with a specific mode of natural variability of the system. This dramatically improves the interpretability of the resulting response formulas. We show here on three simple yet mathematically meaningful examples how to use the Extended Dynamical Mode Decomposition (EDMD) algorithm on an individual trajectory of the system to compute with high accuracy correlation functions as well as Green functions associated with acting forcings. This demonstrates the great potential of using Koopman analysis for the key problem of evaluating and testing the sensitivity of a complex system.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Deficiency of equation-finding approach to data-driven modeling of dynamical systems

    nlin.CD 2025-09 conditional novelty 6.0 of 10

    Different SINDy-style equations recovered from different flawed measurements of a chaotic system can produce statistically indistinguishable attractors and matching leading Koopman spectra.

  2. Markov matrix perturbations to optimize dynamical and entropy functionals

    math.DS 2025-07 conditional novelty 6.0 of 10

    Linear-response optimization algorithms are derived for entropy, KL divergence, and entropy production on Markov chains, with a drift-reconstruction protocol linking matrix perturbations to vector field forcing.

  3. Interpretable and Equation-Free Response Theory for Complex Systems

    cond-mat.stat-mech 2025-02 conditional novelty 6.0 of 10

    For Markov chains, linear and nonlinear response to time-dependent forcings can be written as sums of exponentials governed by the chain's Koopman eigenvalues, enabling equation-free response prediction.

Pith tools