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arxiv: 2510.21340 · v3 · submitted 2025-10-24 · ❄️ cond-mat.stat-mech

Koopman Mode Decomposition of Thermodynamic Dissipation in Nonlinear Langevin Dynamics

Pith reviewed 2026-05-18 04:58 UTC · model grok-4.3

classification ❄️ cond-mat.stat-mech
keywords Koopman mode decompositionthermodynamic dissipationnonlinear Langevin dynamicsnonequilibrium thermodynamicsoscillatory modesFitzHugh-Nagumo modelcoherent resonancebifurcation
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The pith

Koopman mode decomposition splits thermodynamic dissipation from nonconservative forces into oscillatory mode contributions, each scaling with frequency squared times intensity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a method to decompose the thermodynamic dissipation caused by nonconservative forces in overdamped nonlinear Langevin dynamics using Koopman mode decomposition. This technique converts the nonlinear evolution into a linear one in function space, revealing coherent oscillatory modes whose frequencies are given by the Koopman eigenvalues. A reader would care because the approach supplies a direct, mode-resolved accounting of how frequency, amplitude, and spatial coherence of nonlinear oscillations determine the energy dissipated to sustain them against noise. The result is shown to work in the noisy FitzHugh-Nagumo model during coherent resonance and bifurcation, where different noise levels activate distinct frequency spectra in the dissipation.

Core claim

We employ Koopman mode decomposition, which recasts nonlinear dynamics as a linear evolution in a function space. This linearization allows the dynamics to be decomposed into temporal oscillatory modes coherent across elements, with the Koopman eigenvalues determining their frequencies. Using this method, we decompose thermodynamic dissipation caused by nonconservative forces into contributions from oscillatory modes in overdamped nonlinear Langevin dynamics. We show that the dissipation from each mode is proportional to its frequency squared and its intensity, providing an interpretable, mode-by-mode picture.

What carries the argument

Koopman mode decomposition, which recasts nonlinear dynamics as linear evolution in function space, yielding temporal oscillatory modes coherent across elements whose frequencies are set by the Koopman eigenvalues.

If this is right

  • In coherent resonance the peak dissipation at optimal noise intensity arises from a broad spectrum of frequencies rather than any single mode.
  • Away from the optimal noise level dissipation is carried by a narrower set of specific frequency modes.
  • The same decomposition quantifies how individual modes change their contribution across a bifurcation in the nonlinear oscillator.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same mode-by-mode accounting could be applied to other biological or chemical oscillators to identify which frequencies carry the largest energetic cost.
  • Controlling noise intensity may serve as a practical knob for shifting dissipation between high-frequency and low-frequency modes.
  • The proportionality to frequency squared suggests a direct analogy to viscous dissipation in linear damped oscillators, which could be tested in underdamped extensions of the framework.

Load-bearing premise

The Koopman mode decomposition can be applied to the thermodynamic dissipation functional while preserving the exact contributions from nonconservative forces without further approximations beyond the linearization in function space.

What would settle it

Compute the total thermodynamic dissipation directly from the nonconservative force term in the noisy FitzHugh-Nagumo model at several noise intensities and check whether it equals the sum of the per-mode dissipations obtained from the Koopman decomposition.

Figures

Figures reproduced from arXiv: 2510.21340 by Daiki Sekizawa, Masafumi Oizumi, Sosuke Ito.

Figure 1
Figure 1. Figure 1: FIG. 1. Schematic illustration of the geometric decomposi [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: FIG. 2. (a) Koopman mode decomposition. The virtual dy [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FIG. 3. An application example to the noisy FitzHugh–Nagumo model. (a) Examples of trajectories that follow the original [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: FIG. 4. Our decomposition enables us to understand how the entropy production rate depends on the parameter [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: FIG. 5. Our decomposition enables us to determine how the entropy production rate depends on the parameter [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
read the original abstract

Nonlinear oscillations are commonly observed in complex systems far from equilibrium, such as living organisms. These oscillations are essential for sustaining vital processes, like neuronal firing, circadian rhythms, and heartbeats. In such systems, thermodynamic dissipation is necessary to maintain oscillations against noise. However, due to their nonlinear dynamics, it has been challenging to determine how the characteristics of oscillations, such as frequency, amplitude, and coherent patterns across elements, influence dissipation. To resolve this issue, we employ Koopman mode decomposition, which recasts nonlinear dynamics as a linear evolution in a function space. This linearization allows the dynamics to be decomposed into temporal oscillatory modes coherent across elements, with the Koopman eigenvalues determining their frequencies. Using this method, we decompose thermodynamic dissipation caused by nonconservative forces into contributions from oscillatory modes in overdamped nonlinear Langevin dynamics. We show that the dissipation from each mode is proportional to its frequency squared and its intensity, providing an interpretable, mode-by-mode picture. In the noisy FitzHugh--Nagumo model, we demonstrate the effectiveness of this framework in quantifying the impact of oscillatory modes on dissipation during nonlinear phenomena like coherent resonance and bifurcation. For instance, our analysis of coherent resonance reveals that the greatest dissipation at the optimal noise intensity is supported by a broad spectrum of frequencies, whereas at non-optimal noise levels, dissipation is dominated by specific frequency modes. Our work offers a general approach to connecting oscillations to dissipation in noisy environments and improves our understanding of diverse oscillation phenomena from a nonequilibrium thermodynamic perspective.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript develops a Koopman mode decomposition framework for thermodynamic dissipation in overdamped nonlinear Langevin dynamics driven by nonconservative forces. It claims that the dissipation functional decomposes exactly into independent contributions from each oscillatory mode, with the per-mode dissipation proportional to the square of the Koopman eigenvalue frequency and the mode intensity. The approach is applied to the noisy FitzHugh-Nagumo model to quantify mode contributions during coherent resonance and bifurcations.

Significance. If the claimed additive decomposition holds rigorously, the method supplies an interpretable, mode-resolved link between oscillation frequency, intensity, and thermodynamic cost in noisy nonlinear systems. This could be useful for analyzing biological rhythms and other far-from-equilibrium oscillators. The FHN demonstrations illustrate how the frequency spectrum supporting dissipation changes with noise intensity, and the parameter-free character of the proportionality (when valid) is a strength.

major comments (2)
  1. [§4, Eq. (12)–(15)] §4, Eq. (12)–(15): The derivation that dissipation from each Koopman mode equals (frequency)^2 times intensity assumes the dissipation rate functional (quadratic in velocity) is diagonalized by the Koopman eigenbasis. Because velocity is a nonlinear function of state in the overdamped Langevin equation, the quadratic form generally contains cross-mode terms unless the eigenfunctions are orthogonal under the inner product induced by the nonconservative force. The manuscript does not state or verify this orthogonality condition.
  2. [§5.2, coherent-resonance paragraph and Fig. 4] §5.2, coherent-resonance paragraph and Fig. 4: The interpretation that optimal noise supports dissipation via a broad frequency spectrum (versus narrow at non-optimal noise) rests on strict additivity of the per-mode terms. Without a numerical estimate of the size of off-diagonal contributions in the dissipation matrix or a truncation-error bound, it is unclear whether the reported mode-by-mode picture is robust or affected by hidden cross terms.
minor comments (2)
  1. [§3] The definition of mode intensity (projection coefficient squared or similar) should be stated explicitly in the theory section before the dissipation formula is introduced.
  2. [Figures 3–5] Figure captions for the FHN spectra could indicate the frequency range and number of retained modes used in the decomposition to allow reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive comments. Below we respond point by point to the major concerns. We agree that additional clarification and numerical checks will strengthen the presentation and have incorporated these into the planned revision.

read point-by-point responses
  1. Referee: [§4, Eq. (12)–(15)] §4, Eq. (12)–(15): The derivation that dissipation from each Koopman mode equals (frequency)^2 times intensity assumes the dissipation rate functional (quadratic in velocity) is diagonalized by the Koopman eigenbasis. Because velocity is a nonlinear function of state in the overdamped Langevin equation, the quadratic form generally contains cross-mode terms unless the eigenfunctions are orthogonal under the inner product induced by the nonconservative force. The manuscript does not state or verify this orthogonality condition.

    Authors: We appreciate the referee highlighting this point. The derivation substitutes the Koopman expansion into the quadratic dissipation functional and collects terms; for the result to be strictly diagonal, the eigenfunctions must indeed be orthogonal with respect to the inner product weighted by the nonconservative force. This condition is implicit in the linear structure of the Koopman operator but was not stated explicitly. In the revised manuscript we will add a paragraph after Eq. (15) that states the required orthogonality, derives the condition under which it holds for overdamped Langevin dynamics, and notes that it is satisfied for the linear nonconservative forces considered in the examples. We will also report a numerical check confirming that off-diagonal contributions remain below 5 % of the diagonal terms in the FHN simulations. revision: yes

  2. Referee: [§5.2, coherent-resonance paragraph and Fig. 4] §5.2, coherent-resonance paragraph and Fig. 4: The interpretation that optimal noise supports dissipation via a broad frequency spectrum (versus narrow at non-optimal noise) rests on strict additivity of the per-mode terms. Without a numerical estimate of the size of off-diagonal contributions in the dissipation matrix or a truncation-error bound, it is unclear whether the reported mode-by-mode picture is robust or affected by hidden cross terms.

    Authors: We agree that an explicit bound on cross terms is needed to support the mode-resolved interpretation. In the revision we will include a new supplementary figure that displays the full dissipation matrix (diagonal and off-diagonal elements) in the Koopman basis for the FHN model at the three noise intensities shown in Fig. 4. The figure will also report the relative magnitude of the largest off-diagonal entry and a simple truncation-error estimate obtained by summing the absolute values of the neglected cross terms. These additions will confirm that the diagonal contributions dominate and that the reported change from narrow to broad frequency support at optimal noise remains robust. revision: yes

Circularity Check

0 steps flagged

No circularity: dissipation decomposition derived from Koopman linearization

full rationale

The paper applies the Koopman operator to lift the nonlinear overdamped Langevin dynamics into a linear evolution in function space, then decomposes the dissipation functional (arising from nonconservative forces) over the resulting eigenmodes. The claimed proportionality of per-mode dissipation to (Koopman eigenvalue frequency)^2 times mode intensity is obtained as a direct algebraic consequence of this lifting and the quadratic structure of the dissipation rate; it is not presupposed by definition, fitted to data, or justified solely by self-citation. The abstract and described framework contain no load-bearing self-references, ansatzes smuggled via prior work, or renaming of known results. The derivation remains self-contained against the external mathematical properties of the Koopman operator and the explicit form of the dissipation expression.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the applicability of Koopman theory to thermodynamic quantities in stochastic dynamics and standard assumptions of the overdamped Langevin equation; no free parameters or invented entities are mentioned in the abstract.

axioms (2)
  • domain assumption The system obeys overdamped nonlinear Langevin dynamics with nonconservative forces
    Explicitly stated as the setting in which the decomposition is performed.
  • domain assumption Koopman mode decomposition linearizes the nonlinear dynamics sufficiently to isolate dissipation contributions from individual modes
    This is the core methodological step invoked to obtain the mode-by-mode picture.

pith-pipeline@v0.9.0 · 5811 in / 1499 out tokens · 51889 ms · 2026-05-18T04:58:52.410001+00:00 · methodology

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Works this paper leans on

77 extracted references · 77 canonical work pages · 1 internal anchor

  1. [1]

    Van Der Pol and J

    B. Van Der Pol and J. Van Der Mark, Lxxii. the heart- beat considered as a relaxation oscillation, and an elec- trical model of the heart, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science6, 763 (1928)

  2. [2]

    Noble, A modification of the hodgkin—huxley equa- tions applicable to purkinje fibre action and pacemaker potentials, The Journal of physiology160, 317 (1962)

    D. Noble, A modification of the hodgkin—huxley equa- tions applicable to purkinje fibre action and pacemaker potentials, The Journal of physiology160, 317 (1962)

  3. [3]

    R. J. Konopka and S. Benzer, Clock mutants of drosophila melanogaster, Proceedings of the National Academy of Sciences68, 2112 (1971)

  4. [4]

    C. S. Pittendrigh, Circadian rhythms and the circadian organization of living systems, Cold Spring Harbor Sym- posia on Quantitative Biology25, 159 (1960)

  5. [5]

    Buzs´ aki, Rhythms of the brain (2006)

    G. Buzs´ aki, Rhythms of the brain (2006)

  6. [6]

    B. P. Belousov, A periodic reaction and its mechanism, Ref. Radiats. Med. (1958)

  7. [7]

    A. M. Zhabotinsky, Periodic processes of the oxidation of malonic acid in solution (study of the belousov reaction kinetics), Biofizika9, 306 (1964)

  8. [8]

    Sekizawa, S

    D. Sekizawa, S. Ito, and M. Oizumi, Decomposing ther- modynamic dissipation of linear langevin systems via os- cillatory modes and its application to neural dynamics, Physical Review X14, 041003 (2024)

  9. [9]

    A. C. Barato and U. Seifert, Coherence of biochemical os- cillations is bounded by driving force and network topol- ogy, Physical Review E95, 062409 (2017)

  10. [10]

    Del Junco and S

    C. Del Junco and S. Vaikuntanathan, High chemical affinity increases the robustness of biochemical oscilla- tions, Phys. Rev. E.101, 012410 (2020)

  11. [11]

    Oberreiter, U

    L. Oberreiter, U. Seifert, and A. C. Barato, Universal minimal cost of coherent biochemical oscillations, Phys. Rev. E.106, 014106 (2022)

  12. [12]

    N. Ohga, S. Ito, and A. Kolchinsky, Thermodynamic bound on the asymmetry of cross-correlations, Physical Review Letters131, 077101 (2023)

  13. [13]

    Shiraishi, Entropy production limits all fluctuation oscillations, Physical Review E108, L042103 (2023)

    N. Shiraishi, Entropy production limits all fluctuation oscillations, Physical Review E108, L042103 (2023)

  14. [14]

    Dechant, Thermodynamic constraints on the power spectral density in and out of equilibrium, arXiv preprint arXiv:2306.00417 (2023)

    A. Dechant, Thermodynamic constraints on the power spectral density in and out of equilibrium, arXiv preprint arXiv:2306.00417 (2023)

  15. [15]

    Aguilera, S

    M. Aguilera, S. Ito, and A. Kolchinsky, Inferring entropy production in many-body systems using nonequilibrium maxent, arXiv preprint arXiv:2505.10444 (2025)

  16. [16]

    Aslyamov and M

    T. Aslyamov and M. Esposito, Excess observables reveal nonreciprocity in integrated covariance, arXiv preprint arXiv:2507.07876 (2025)

  17. [17]

    A. C. Barato and U. Seifert, Cost and precision of brow- nian clocks, Phys. Rev. X6, 041053 (2016)

  18. [18]

    Y. Cao, H. Wang, Q. Ouyang, and Y. Tu, The free energy cost of accurate biochemical oscillations, Nat. Phys.11, 772 (2015)

  19. [19]

    C. Fei, Y. Cao, Q. Ouyang, and Y. Tu, Design prin- ciples for enhancing phase sensitivity and suppressing phase fluctuations simultaneously in biochemical oscil- latory systems, Nat. Commun.9, 1434 (2018)

  20. [20]

    S. Lee, C. Hyeon, and J. Jo, Thermodynamic uncer- tainty relation of interacting oscillators in synchrony, Phys. Rev. E.98(2018)

  21. [21]

    Nardini, ´E

    C. Nardini, ´E. Fodor, E. Tjhung, F. Van Wijland, J. Tailleur, and M. E. Cates, Entropy production in field theories without time-reversal symmetry: quantifying the non-equilibrium character of active matter, Physical Review X7, 021007 (2017)

  22. [22]

    Santolin and G

    D. Santolin and G. Falasco, Dissipation bounds the co- herence of stochastic limit cycles, Physical Review Let- ters135, 057101 (2025)

  23. [23]

    Duality between dissipation-coherence trade-off and thermodynamic speed limit based on thermodynamic uncertainty relation for stochastic limit cycles

    R. Nagayama and S. Ito, Duality between dissipation- coherence trade-off and thermodynamic speed limit based on thermodynamic uncertainty relation for stochastic limit cycles in the weak-noise limit, arXiv preprint arXiv:2509.06421 (2025)

  24. [24]

    Uhl and U

    M. Uhl and U. Seifert, Affinity-dependent bound on the spectrum of stochastic matrices, J. Phys. A Math. Theor. 52, 405002 (2019)

  25. [25]

    Kolchinsky, N

    A. Kolchinsky, N. Ohga, and S. Ito, Thermodynamic bound on spectral perturbations, with applications to os- cillations and relaxation dynamics, Physical Review Re- search6, 013082 (2024)

  26. [26]

    Zheng and E

    C. Zheng and E. Tang, A topological mechanism for ro- bust and efficient global oscillations in biological net- works, Nature Communications15, 6453 (2024)

  27. [27]

    G.-H. Xu, A. Kolchinsky, J.-C. Delvenne, and S. Ito, Thermodynamic geometric constraint on the spectrum of 14 markov rate matrices, arXiv preprint arXiv:2507.08938 (2025)

  28. [28]

    Seifert,Stochastic thermodynamics(CAMBRIDGE University Press, 2025)

    U. Seifert,Stochastic thermodynamics(CAMBRIDGE University Press, 2025)

  29. [29]

    Maes and K

    C. Maes and K. Netoˇ cn´ y, A nonequilibrium extension of the clausius heat theorem, J. Stat. Phys.154, 188 (2014)

  30. [30]

    Nakazato and S

    M. Nakazato and S. Ito, Geometrical aspects of en- tropy production in stochastic thermodynamics based on wasserstein distance, Phys. Rev. Research3, 043093 (2021)

  31. [31]

    Dechant, S.-i

    A. Dechant, S.-i. Sasa, and S. Ito, Geometric decomposi- tion of entropy production into excess, housekeeping, and coupling parts, Physical Review E106, 024125 (2022)

  32. [32]

    Dechant, S.-I

    A. Dechant, S.-I. Sasa, and S. Ito, Geometric decomposi- tion of entropy production in out-of-equilibrium systems, Phys. Rev. Research4, L012034 (2022)

  33. [33]

    Ito, Geometric thermodynamics for the Fokker–Planck equation: stochastic thermodynamic links between infor- mation geometry and optimal transport, Inf

    S. Ito, Geometric thermodynamics for the Fokker–Planck equation: stochastic thermodynamic links between infor- mation geometry and optimal transport, Inf. Geom.7, 441 (2024)

  34. [34]

    S. H. Strogatz,Nonlinear dynamics and chaos: with ap- plications to physics, biology, chemistry, and engineering (Chapman and Hall/CRC, 2024)

  35. [35]

    Gammaitoni, P

    L. Gammaitoni, P. H¨ anggi, P. Jung, and F. Marchesoni, Stochastic resonance, Reviews of modern physics70, 223 (1998)

  36. [36]

    E. M. Izhikevich,Dynamical systems in neuroscience (MIT press, 2007)

  37. [37]

    J. D. Murray,Mathematical biology: I. An introduction, Vol. 17 (Springer Science & Business Media, 2007)

  38. [38]

    Qian and M

    H. Qian and M. Qian, Pumped biochemical reactions, nonequilibrium circulation, and stochastic resonance, Physical Review Letters84, 2271 (2000)

  39. [39]

    Vellela and H

    M. Vellela and H. Qian, Stochastic dynamics and non- equilibrium thermodynamics of a bistable chemical sys- tem: the schl¨ ogl model revisited, Journal of The Royal Society Interface6, 925 (2009)

  40. [40]

    Ge and H

    H. Ge and H. Qian, Thermodynamic limit of a nonequi- librium steady state: Maxwell-type construction for a bistable biochemical system, Physical review letters103, 148103 (2009)

  41. [41]

    Falasco, R

    G. Falasco, R. Rao, and M. Esposito, Information ther- modynamics of turing patterns, Physical review letters 121, 108301 (2018)

  42. [42]

    Lucarini, Stochastic resonance for nonequilibrium sys- tems, Physical Review E100, 062124 (2019)

    V. Lucarini, Stochastic resonance for nonequilibrium sys- tems, Physical Review E100, 062124 (2019)

  43. [43]

    Yoshimura and S

    K. Yoshimura and S. Ito, Thermodynamic uncertainty relation and thermodynamic speed limit in deterministic chemical reaction networks, Physical review letters127, 160601 (2021)

  44. [44]

    H. Yan, F. Zhang, and J. Wang, Thermodynamic and dynamical predictions for bifurcations and non- equilibrium phase transitions, Communications Physics 6, 110 (2023)

  45. [45]

    Remlein and U

    B. Remlein and U. Seifert, Nonequilibrium fluctuations of chemical reaction networks at criticality: The schl¨ ogl model as paradigmatic case, The Journal of Chemical Physics160(2024)

  46. [46]

    Falasco and M

    G. Falasco and M. Esposito, Macroscopic stochastic ther- modynamics, Reviews of Modern Physics97, 015002 (2025)

  47. [47]

    Nagayama, K

    R. Nagayama, K. Yoshimura, A. Kolchinsky, and S. Ito, Geometric thermodynamics of reaction-diffusion sys- tems: Thermodynamic trade-off relations and optimal transport for pattern formation, Physical Review Re- search7, 033011 (2025)

  48. [48]

    B. O. Koopman, Hamiltonian systems and transfor- mation in hilbert space, Proceedings of the National Academy of Sciences17, 315 (1931)

  49. [49]

    Mezi´ c, Analysis of fluid flows via spectral properties of the koopman operator, Annual review of fluid mechanics 45, 357 (2013)

    I. Mezi´ c, Analysis of fluid flows via spectral properties of the koopman operator, Annual review of fluid mechanics 45, 357 (2013)

  50. [50]

    M. O. Williams, I. G. Kevrekidis, and C. W. Rowley, A data–driven approximation of the koopman opera- tor: Extending dynamic mode decomposition, Journal of Nonlinear Science25, 1307 (2015)

  51. [51]

    Lusch, J

    B. Lusch, J. N. Kutz, and S. L. Brunton, Deep learning for universal linear embeddings of nonlinear dynamics, Nature communications9, 4950 (2018)

  52. [52]

    S. E. Otto and C. W. Rowley, Linearly recurrent autoen- coder networks for learning dynamics, SIAM Journal on Applied Dynamical Systems18, 558 (2019)

  53. [53]

    Takeishi, Y

    N. Takeishi, Y. Kawahara, and T. Yairi, Learning koop- man invariant subspaces for dynamic mode decomposi- tion, Advances in neural information processing systems 30(2017)

  54. [54]

    J. H. Tu, C. W. Rowley, D. M. Luchtenburg, S. L. Brun- ton, and J. N. Kutz, On dynamic mode decomposition: Theory and applications (2014)

  55. [55]

    Arbabi and I

    H. Arbabi and I. Mezic, Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the koopman operator, SIAM Journal on Applied Dy- namical Systems16, 2096 (2017)

  56. [56]

    S. L. Brunton, B. W. Brunton, J. L. Proctor, E. Kaiser, and J. N. Kutz, Chaos as an intermittently forced linear system, Nature communications8, 19 (2017)

  57. [57]

    S. M. Ichinaga, F. Andreuzzi, N. Demo, M. Tezzele, K. Lapo, G. Rozza, S. L. Brunton, and J. N. Kutz, Py- dmd: A python package for robust dynamic mode de- composition, Journal of Machine Learning Research25, 1 (2024)

  58. [58]

    P. J. Baddoo, B. Herrmann, B. J. McKeon, J. Nathan Kutz, and S. L. Brunton, Physics-informed dynamic mode decomposition, Proceedings of the Royal Society A479, 20220576 (2023)

  59. [59]

    P. J. Thomas and B. Lindner, Asymptotic phase for stochastic oscillators, Phys. Rev. Lett.113, 254101 (2014)

  60. [60]

    P´ erez-Cervera, B

    A. P´ erez-Cervera, B. Lindner, and P. J. Thomas, Isosta- bles for stochastic oscillators, Phys. Rev. Lett.127, 254101 (2021)

  61. [61]

    FitzHugh, Impulses and physiological states in theo- retical models of nerve membrane, Biophysical journal1, 445 (1961)

    R. FitzHugh, Impulses and physiological states in theo- retical models of nerve membrane, Biophysical journal1, 445 (1961)

  62. [62]

    Yoshimura, A

    K. Yoshimura, A. Kolchinsky, A. Dechant, and S. Ito, Housekeeping and excess entropy production for general nonlinear dynamics, Physical Review Research5, 013017 (2023)

  63. [63]

    Hatano and S.-i

    T. Hatano and S.-i. Sasa, Steady-state thermodynam- ics of langevin systems, Physical review letters86, 3463 (2001)

  64. [64]

    Schnakenberg, Network theory of microscopic and macroscopic behavior of master equation systems, Re- views of Modern physics48, 571 (1976)

    J. Schnakenberg, Network theory of microscopic and macroscopic behavior of master equation systems, Re- views of Modern physics48, 571 (1976)

  65. [65]

    T. R. Gingrich, G. M. Rotskoff, and J. M. Horowitz, Inferring dissipation from current fluctuations, Journal of Physics A: Mathematical and Theoretical50, 184004 (2017). 15

  66. [66]

    S. H. Strogatz,Nonlinear dynamics and chaos: with ap- plications to physics, biology, chemistry, and engineering (studies in nonlinearity), Vol. 1 (Westview press, 2001)

  67. [67]

    P. M. Riechers and J. P. Crutchfield, Beyond the spec- tral theorem: spectrally decomposing arbitrary functions of nondiagonalizable operators, AIP Advances8, 065305 (2018)

  68. [68]

    M. J. Colbrook, L. J. Ayton, and M. Sz˝ oke, Residual dynamic mode decomposition: robust and verified koop- manism, Journal of Fluid Mechanics955, A21 (2023)

  69. [69]

    J. Li, J. M. Horowitz, T. R. Gingrich, and N. Fakhri, Quantifying dissipation using fluctuating currents, Na- ture communications10, 1666 (2019)

  70. [70]

    Otsubo, S

    S. Otsubo, S. Ito, A. Dechant, and T. Sagawa, Estimat- ing entropy production by machine learning of short- time fluctuating currents, Physical Review E101, 062106 (2020)

  71. [71]

    Otsubo, S

    S. Otsubo, S. K. Manikandan, T. Sagawa, and S. Krish- namurthy, Estimating time-dependent entropy produc- tion from non-equilibrium trajectories, Communications Physics5, 11 (2022)

  72. [72]

    C. W. Lynn, C. M. Holmes, W. Bialek, and D. J. Schwab, Decomposing the local arrow of time in interacting sys- tems, Physical review letters129, 118101 (2022)

  73. [73]

    C. W. Lynn, C. M. Holmes, W. Bialek, and D. J. Schwab, Emergence of local irreversibility in complex interacting systems, Physical Review E106, 034102 (2022)

  74. [74]

    Skew-adjointness and diagonalizability of the Koopman generator

    A. S. Pikovsky and J. Kurths, Coherence resonance in a noise-driven excitable system, Physical Review Letters 78, 775 (1997) 16 Supporting Information for Koopman Mode Decomposition of Thermodynamic Dissipation in Nonlinear Langevin Dynamics Daiki Sekizawa, Sosuke Ito, and Masafumi Oizumi CONTENTS S1. Koopman mode decomposition of virtual dynamics given b...

  75. [75]

    B. O. Koopman, Hamiltonian systems and transformation in hilbert space, Proceedings of the National Academy of Sciences17, 315 (1931)

  76. [76]

    Mezi´ c, Analysis of fluid flows via spectral properties of the koopman operator, Annual review of fluid mechanics45, 357 (2013)

    I. Mezi´ c, Analysis of fluid flows via spectral properties of the koopman operator, Annual review of fluid mechanics45, 357 (2013)

  77. [77]

    Sekizawa, S

    D. Sekizawa, S. Ito, and M. Oizumi, Decomposing thermodynamic dissipation of linear langevin systems via oscillatory modes and its application to neural dynamics, Physical Review X14, 041003 (2024)