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Synchronization by noise for traveling pulses

T0 review · 2 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

Pith's one-line read The paper proves that two traveling-pulse solutions driven by identical multiplicative noise synchronize in probability on intermediate time scales.

desk verdict Strong, likely-correct phase-reduction proof that traveling pulses synchronize by noise, with the main caveat being an outsourced co-author preprint for the key time-scale estimate. read the letter →

arxiv 2501.13565 v1 pith:25KN7J25 submitted 2025-01-23 math.PR math.APmath.DS

classification math.PRmath.APmath.DS MSC 60H1537H1537L3037L10
keywords travelingpulsesynchronizationbynoisephasereductionstochasticpartialdifferentialequationisochronalmultiplicativeFitzHugh–NagumoLyapunovexponent
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper proves that two traveling-pulse solutions driven by the same realization of a multiplicative noise synchronize in probability: starting from different initial positions, they converge to one another modulo integer spatial shifts, on the intermediate time scale $\sigma^{-2}\ll t_\sigma\le\exp(\sigma^{-2+q})$ as the noise amplitude $\sigma$ tends to zero. The authors state this is the first rigorous proof of synchronization by noise for traveling pulses, a phenomenon previously supported mainly by numerics and heuristics. The proof does not attack the infinite-dimensional SPDE directly; instead, it reduces the dynamics to the scalar position (phase) of the pulse, proves that this phase synchronizes using existing abstract criteria and a negative Lyapunov exponent, and then transfers the synchronization back to the full solution using error estimates valid on the required time scales.

What carries the argument

The central object is the isochronal phase map $\pi$, which assigns to every profile in the pulse's basin of attraction the unique shift $s\in\mathbb{R}$ such that the deterministic flow converges to $u_*(\cdot-s)$. Applying an Itô formula to $\pi(u_\sigma)$ yields the scalar reduced SDE $$ d\gamma_\$\sigma$ = c\,dt+\$sigma^{2}$ a(\gamma_\$\sigma$)\,dt+\$\sigma$\sum_{k\in\mathbb{Z}} b_k(\gamma_\$\sigma$)\,d\beta_k(t), $$ whose coefficients inherit the translational symmetry of the PDE; in particular $b_k$ is given explicitly by $\alpha_k\langle \psi g(u_*),T_{-x}e_k\rangle$, so the lowest Fourier modes rotate the phase. Synchronization of this SDE is obtained from the abstract criteria in [29] by checking exponential mixing, irreducibility/controllability, and strict negativity of the Lyapunov exponent, computed as $\lambda=-\frac12\sigma^2\sum_k\int_{\mathbb{T}}|\partial_x(b_k p)|^2/p\,dx$ with $p$ the invariant density. Assumption 4 (either $c=0$ or spatially homogeneous noise) is then used to rescale the SDE into a $\sigma$-independent equation, giving a synchronization rate uniform in the initial condition; Theorems 3.8 and 3.9 certify that the phase reduction remains accurate on a longer time scale than the synchronization time.

What would settle it

Run two simulations of the same stochastic pulse equation from different initial shifts using one noise realization, at small $\sigma$ and observation time $t_\sigma\approx\sigma^{-2}\log(\sigma^{-1})$; if $\inf_n\|u^x_\sigma(t_\sigma,\cdot+n)-u^y_\sigma(t_\sigma,\cdot)\|_X$ does not go to zero in probability as $\sigma\to 0$, Theorem 1 is false. A sharper check targets the black box: sample the maximum deviation of $u_\sigma$ from its isochronal translate over $t\le\exp(\sigma^{-2+3q})$; if that deviation exceeds $\sigma^q$ with probability larger than $\exp(-\sigma^{-2+3q})$, the proof's transfer step fails regardless of the reduced SDE.

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Extended reading notes

Core claim

Under Assumptions 1–4, the central result is Theorem 1: for any initial positions $x,y\in\mathbb{R}$ and any time family $(t_\sigma)_{\sigma>0}$ with $\sigma^{-2}\ll t_\sigma\le \exp(\sigma^{-2+q})$ for some $q\in(0,2)$, the mild solutions $u^x_\sigma$ and $u^y_\sigma$ starting from $u_*(\cdot-x)$ and $u_*(\cdot-y)$ satisfy $$ \inf_{n\in\mathbb{Z}}\|u^x_\$\sigma$(t_\$\sigma$,\cdot+n)-u^y_\$\sigma$(t_\$\sigma$,\cdot)\|_X \xrightarrow{P} 0 \quad\text{as }\$\sigma$\to 0. $$ The infimum over $n$ reflects the spatial periodicity of the noise; on a periodic domain it can be dropped. The proof isolates the isochronal phase $\pi(u_\sigma)$ as the only relevant degree of freedom, shows the phase-reduced SDE synchronizes on the $\sigma^{-2}$ scale uniformly in the initial condition (under Assumption 4), and then uses the approximation theorems to carry the synchronization back to the full solutions. This turns synchronization by noise for pulses from a numerically observed phenomenon into a proved statement.

Load-bearing premise

Everything rests on the imported estimate that a noise-driven pulse keeps resembling some translate of the deterministic pulse profile for exponentially long times with overwhelming probability; if that estimate is wrong, the phase-reduction window and with it the synchronization transfer collapse.

Editorial extensions

If this is right

  • For the FitzHugh–Nagumo equation with the stated noise, two pulses started at different locations and driven by identical noise converge to one another modulo translation in probability on the window $\sigma^{-2}\ll t\ll\exp(\sigma^{-2})$.
  • The mechanism does not need strong nondegeneracy: nondegeneracy only in the lowest Fourier mode, plus condition (2.5), is enough.
  • The synchronization rate is uniform in the initial phase whenever Assumption 4 holds, so the reduced dynamics admits a single random point attractor rather than a random set.
  • On a periodic spatial domain, the same argument gives synchronization without the modulo-integer-translation caveat.
  • The proof lays out a template for rigorous synchronization results via phase reduction: reduce, synchronize the phase, and transfer back within the validity window.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the only real obstruction to dropping Assumption 4 seems to be quantitative control of the synchronization rate; a quantitative version of the abstract synchronization criterion would likely extend Theorem 1 to arbitrary periodic noise.
  • Editorial inference: for higher-dimensional patterns (spiral waves, multidimensional fronts), the reduced dynamics would have more than one phase component, so the same strategy would predict either synchronization under a negative top Lyapunov exponent or chaos under a positive one.
  • Editorial inference: the intermediate window between $\sigma^{-2}$ and $\exp(\sigma^{-2})$ is a concrete, falsifiable prediction for neural or cardiac models: synchronized pulses should be observable only inside that window, with desynchronization expected after exponentially long times.
  • Editorial inference: since only $k=\pm1$ modes are needed, truncating the noise to the lowest Fourier mode in simulations should already reproduce the synchronization effect, providing a cheap numerical test of the theorem.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. The manuscript proves a synchronization-by-noise theorem for traveling pulse solutions of a class of semilinear SPDEs, with the stochastic FitzHugh–Nagumo equation as the motivating example. Under assumptions on the deterministic stability of a traveling pulse (Assumption 2), on the regularity and lowest-mode nondegeneracy of the multiplicative noise (Assumption 3), and on an additional symmetry (either zero pulse speed or spatially homogeneous noise, Assumption 4), Theorem 1 asserts that any two solutions starting from different translates of the pulse profile converge to each other in probability modulo integer shifts on the time window σ^{-2} ≪ t_σ ≤ exp(σ^{-2+q}). The proof strategy is a phase reduction: the isochronal phase π(u_σ) is shown to approximate the pulse position, an autonomous scalar SDE for the phase is derived and analyzed, weak synchronization of this reduced SDE is established using the abstract criteria of Flandoli–Gess–Scheutzow, and the synchronization is transferred back to the full SPDE via a law-preserving time change and a uniformity argument. The paper is clearly written and explicitly identifies which steps require additional structural assumptions.

Significance. If the proof is completed as intended, this is the first rigorous synchronization-by-noise theorem for traveling pulses, a phenomenon previously observed mainly in numerical experiments. The phase-reduction method is a valuable new addition to the small set of analytical tools for synchronization of SPDEs, and the treatment of the reduced SDE is careful and self-contained: it includes an explicit Lyapunov exponent formula, a direct verification of the controllability and transitivity conditions needed in [29], and a precise discussion of why the transfer step fails without Assumption 4. The paper is honest about the intermediate nature of the synchronization window. The main weakness is the paper's dependence, at a load-bearing point, on an unrefereed companion preprint by one of the authors, without stating or verifying the hypotheses of the imported theorem.

major comments (2)
  1. [Section 3.4, proof of Theorem 3.8 (Eq. (3.13))] Theorem 3.8 is the central estimate that keeps the stochastic pulse inside the isochron basin Γ_{C^{-1}σ^q} for the entire time horizon up to exp(σ^{-2+3q}). Its entire proof is the sentence 'choose ε = C^{-1}σ^q and T = exp(σ^{-2+3q}) in [69, Theorem 4.9]' followed by 'After scaling away the constants'. The paper does not state the hypotheses or the precise conclusion of [69, Theorem 4.9], does not verify that Assumptions 1–3 of the present paper satisfy those hypotheses, and does not derive the claimed probability bound exp(-σ^{-2+3q}) from the cited result. This is load-bearing: if [69, Theorem 4.9] requires stronger spatial regularity than (2.4), imposes a lower bound on ε unrelated to the chosen σ^q, or yields a time horizon with a different power of σ, the synchronization window in Theorem 1 collapses. The authors should either state [69, Theorem 4.9] in full and give a detailed parameter-checking proof, or replace the import with a direct proof of Theorem 3.8.
  2. [Section 4.6, proof of Theorem 1 (time-shifted phase reduction)] The proof of Theorem 1 decomposes t_σ = s_σ + c_σ σ^{-2} and then invokes 'a time-shifted version of Theorem 3.9' on the interval [s_σ, t_σ]. Theorem 3.9 as stated applies only to the initial condition u_x^σ(0) = T_x u_* at time 0, while at time s_σ the solution u_x^σ(s_σ) is only known to lie in Γ_{σ^{q/3}} (by Theorem 3.8). A rigorous restart lemma is needed to show that the error between π(u_x^σ(s_σ + ·)) and the solution of the phase-reduced SDE initialized at π(u_x^σ(s_σ)) satisfies the same estimates, and that the parameters involved (in particular the exponent in c_σ ≤ log(σ^{-1})^{1-q/9}) are compatible with the admissible range q∈(0,2/9) of Theorem 3.9. This missing step is essential to converting synchronization of the reduced SDE into synchronization of the phases π(u_x^σ(t_σ)) and π(u_y^σ(t_σ)).
minor comments (5)
  1. [Proposition 3.3] There is a typo: 'dervatives' should be 'derivatives'.
  2. [Remark 2.3] The phrase 'converges almost surely in the Hölder space C^α for α<ν' would be clearer as 'for every α∈(0,ν)'.
  3. [Proof of Theorem 3.9] The final line 'note that 3q < 2/3 by assumption' is terse; since q already satisfies q∈(0,2/9) in the theorem statement, the reader has to track the parameter flow, and the sentence could be rewritten for clarity.
  4. [Section 5.3] The claim that 'The validity of the phase reduction (Theorems 3.8 and 3.9) ... are all established without use of Assumption 4' is inaccurate, because Theorem 3.8 is imported from [69] rather than proved in this paper.
  5. [Lemma 4.11] The proof says 'we can find for any L>0 a control h∈H' making the derivative equal to -L sin(2πγ-η); a few more details on how h is constructed from Lemma 4.3 would help readability.

Circularity Check

1 steps flagged · score 4.0 of 10

Load-bearing stochastic-orbital-stability estimate is delegated to a co-author's preprint, making the transfer step a self-citation.

  1. self citation load bearing [Section 3.4, proof of Theorem 3.8; used again in proof of Theorem 1 (Section 4.6) via Theorem 3.9]
    "Proof of Theorem 3.8. Let C be the constant from (3.3). For σ > 0, choose ε = C^{-1}σ^q and T = exp(σ^{-2+3q}) in [69, Theorem 4.9]. After scaling away the constants from the theorem against appropriate powers of σ^q, we see that P[u^x_σ(t)∈ Γ_{C^{-1}σ^q} for all t∈[0, exp(σ^{-2+3q})]] ≥ 1 − exp(−σ^{−2+3q}), for σ≪_q 1. The desired estimate (3.13) then follows using (3.3)."

    Theorem 3.8 is the sole argument that the stochastic pulse remains in the isochron basin up to exp(σ^{-2+3q}); its entire proof is the instruction to apply [69, Thm 4.9] with ε and T rescaled. Reference [69] is an arXiv preprint by the second author (J. van Winden, 2024), and the present paper neither states its hypotheses nor derives the estimate. Since Theorem 3.9 invokes (3.13) and the proof of Theorem 1 says 'By (1.4) and Theorem 3.8 ... it suffices to prove (4.28)', the transfer from phase synchronization to SPDE synchronization is load-bearing on a self-citation whose content is not independently verified in this paper.

full rationale

The synchronization analysis of the reduced phase SDE is self-contained and independent: ergodicity, exponential mixing, explicit Lyapunov exponent negativity, controllability and the meeting condition are verified directly on the coefficients b_k, and the transfer uses the external criterion of Flandoli–Gess–Scheutzow [29]. The isochron-map identities and the Itô formula from [1,3] are not authored by this paper's authors and are not used to assume the synchronization conclusion. The only circularity-burden item is Theorem 3.8, outsourced to a co-author's preprint; the central theorem would collapse if that bound failed, but the core reduced-SDE claim has independent content. Hence the appropriate score is 4 rather than 0-2 or 6+.

Assumptions & free parameters 0 free parameters · 6 assumptions · 0 invented entities

The paper contains no fitted constants or data; its parameters (noise coefficients, pulse speed, exponents q) are inputs to the theorem. The load-bearing unproved inputs are the four structural Assumptions 1-4 plus three external results: the orbital-stability estimate [69, Thm 4.9] (preprint by co-author), the isochron-map Itô formula [1,3], and the abstract synchronization criteria [29]. The dependence on [69] is the most fragile; the other two are standard but still unverified in this text.

assumptions (6)
  • domain assumption Assumption 2: existence of an orbitally stable traveling pulse u* with a spectral gap; the linearized semigroup decays exponentially on the complement of span{∂_x u*}.
    Invoked in Section 2.2 to define the isochron map and to control the transversal dynamics; if the spectral gap fails (e.g., essential spectrum touching 0), the phase reduction and Theorem 3.8 collapse.
  • domain assumption Assumption 3: noise is white in time, periodic in space, with coefficients satisfying (2.4), α±1 ≠ 0, and the nondegeneracy condition (2.5).
    Guarantees the phase-reduced SDE has nonvanishing lowest-mode noise (Lemma 4.3), which drives the negative Lyapunov exponent and controllability.
  • ad hoc to paper Assumption 4: either c = 0 or the noise is spatially homogeneous (α_k = α_{-k} for all k).
    Used only in Proposition 4.18 to relate the σ-dependent SDE (4.2) to the σ-free SDE (4.25), giving the quantitative synchronization time scale for the transfer back to the SPDE. The authors state in Section 5.3 they conjecture it may be unnecessary.
  • domain assumption Orbital stability estimate for stochastic patterns in Banach spaces, [69, Theorem 4.9] (van Winden, preprint).
    Black box behind Theorem 3.8, which keeps the solution in the basin Γ_{C^{-1}σ^q} up to time exp(σ^{-2+3q}) with overwhelming probability. It is a preprint by the second author and is not machine-checked.
  • domain assumption Itô formula for the isochron map π(u_σ) and existence of five bounded Fréchet derivatives of π, from [1] and [3].
    Used in Section 3.3 to derive (3.10) and Proposition 3.3; the formula is nontrivial for infinite-dimensional systems and is imported rather than proved here.
  • standard math Abstract weak synchronization criteria of Flandoli-Gess-Scheutzow [29, Theorem 2.23].
    The reduced SDE is shown to synchronize by verifying mixing (Prop 4.7), asymptotic stability (Cor 4.10), and irreducibility (Props 4.12, 4.16) against these criteria rather than by a direct argument.

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Pith. "Pith review of Synchronization by noise for traveling pulses." pith.science (2026). https://pith.science/paper/25KN7J25

@misc{pith2026250113565,
  author       = {Pith},
  title        = {Pith review of: Synchronization by noise for traveling pulses},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/25KN7J25}},
  note         = {Machine review of arXiv:2501.13565}
}
abstract

We consider synchronization by noise for stochastic partial differential equations which support traveling pulse solutions, such as the FitzHugh-Nagumo equation. We show that any two pulse-like solutions which start from different positions but are forced by the same realization of a multiplicative noise, converge to each other in probability on a time scale $\sigma^{-2} \ll t \ll \exp(\sigma^{-2})$, where $\sigma$ is the noise amplitude. The noise is assumed to be Gaussian, white in time, colored and periodic in space, and non-degenerate only in the lowest Fourier mode. The proof uses the method of phase reduction, which allows one to describe the dynamics of the stochastic pulse only in terms of its position. The position is shown to synchronize building upon existing results, and the validity of the phase reduction allows us to transfer the synchronization back to the full solution.

Figures

Figures reproduced from arXiv: 2501.13565 by the authors.

Figure 1
Figure 1. Trajectories starting at z2 and z3 ‘squeeze’ near z1 and afterwards return to their initial position. It then only remains to show that such a ‘triple squeezing cycle’ occurs with positive probability. Since this only involves the three-point motion (ϕσ(t, ω, zi))i∈{1,2,3} this can be done similarly to how we showed pointwise strong swift transitivity. Let us now make these ideas rigorous. We begin by exhibiting a c… view at source ↗

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Works this paper leans on

76 extracted references · 52 canonical work pages

  1. [29]

    Synchronization by noise

    F. Flandoli, B. Gess, and M. Scheutzow. “Synchronization by noise”. In:Probability Theory and Related Fields 168.3 (Aug. 1, 2017), pp. 511–556.doi: 10.1007/s00440-016-0716-2

  2. [1]

    Existence, regularity, and a strong Itô formula for the isochronal phase of SPDE

    Z. P. Adams. “Existence, regularity, and a strong Itô formula for the isochronal phase of SPDE”. In:Electronic Communications in Probability29 (Jan. 1, 2024).doi: 10.1214/24-ECP624

  3. [2]

    Quasi-Ergodicity of transient patterns in stochastic reaction-diffusion equations

    Z. P. Adams. “Quasi-Ergodicity of transient patterns in stochastic reaction-diffusion equations”. In:Electronic Journal of Probability29 (Jan. 1, 2024).doi: 10.1214/24-EJP1130

  4. [3]

    The isochronal phase of stochastic PDE and integral equations: metastability and other properties

    Z. P. Adams and J. MacLaurin. “The isochronal phase of stochastic PDE and integral equations: metastability and other properties”. In:Journal of Differential Equations414 (Jan. 2025), pp. 773–816.doi: 10.1016/j. jde.2024.09.002

  5. [4]

    Arnold.Random dynamical systems

    L. Arnold.Random dynamical systems. Springer Monographs in Mathematics. Springer Berlin Heidelberg,

  6. [5]

    Order-preserving random dynamical systems: equilibria, attractors, applications

    L. Arnold and I. Chueshov. “Order-preserving random dynamical systems: equilibria, attractors, applications”. In: Dynamics and Stability of Systems13.3 (Jan. 1998), pp. 265–280.doi: 10.1080/02681119808806264

  7. [6]

    Perfect cocycles through stochastic differential equations

    L. Arnold and M. Scheutzow. “Perfect cocycles through stochastic differential equations”. In:Probability Theory and Related Fields101.1 (Mar. 1995), pp. 65–88.doi: 10.1007/BF01192196

  8. [7]

    Invariant foliations near normally hyperbolic invariant manifolds for semiflows

    P. Bates, K. Lu, and C. Zeng. “Invariant foliations near normally hyperbolic invariant manifolds for semiflows”. In:Transactions of the American Mathematical Society352.10 (June 14, 2000), pp. 4641–4676. doi: 10.1090/S0002-9947-00-02503-4

Show all 76 references
  1. [8]

    Asymptotic behaviour of stochastic flows of diffeomorphisms

    P. H. Baxendale. “Asymptotic behaviour of stochastic flows of diffeomorphisms”. In:Stochastic processes and their applications. Ed. by K. Itô and T. Hida. Lecture Notes in Mathematics 1203. Springer Berlin Heidelberg, 1986, pp. 1–19.doi: 10.1007/BFb0076869

  2. [9]

    Statistical equilibrium and two-point motion for a stochastic flow of diffeomorphisms

    P. H. Baxendale. “Statistical equilibrium and two-point motion for a stochastic flow of diffeomorphisms”. In: Spatial stochastic processes. Ed. by K. S. Alexander and J. C. Watkins. Birkhäuser Boston, 1991, pp. 189–218. doi: 10.1007/978-1-4612-0451-0_9

  3. [10]

    A regularity method for lower bounds on the Lyapunov exponent for stochastic differential equations

    J. Bedrossian, A. Blumenthal, and S. Punshon-Smith. “A regularity method for lower bounds on the Lyapunov exponent for stochastic differential equations”. In:Inventiones mathematicae227.2 (Feb. 2022), pp. 429–516. doi: 10.1007/s00222-021-01069-7

  4. [11]

    Ergodic properties of Markov processes

    L. R. Bellet. “Ergodic properties of Markov processes”. In:Open Quantum Systems II. Ed. by S. Attal, A. Joye, and C.-A. Pillet. Vol. 1881. Springer Berlin Heidelberg, 2006, pp. 1–39.doi: 10.1007/3-540-33966-3_1

  5. [12]

    Additive noise destroys the random attractor close to bifurcation

    L. A. Bianchi, D. Blömker, and M. Yang. “Additive noise destroys the random attractor close to bifurcation”. In: Nonlinearity 29.12 (Nov. 2016), p. 3934.doi: 10.1088/0951-7715/29/12/3934. 25

  6. [13]

    On the pitchfork bifurcation for the Chafee–Infante equation with additive noise

    A. Blumenthal, M. Engel, and A. Neamţu. “On the pitchfork bifurcation for the Chafee–Infante equation with additive noise”. In:Probability Theory and Related Fields187.3-4 (Dec. 2023), pp. 603–627. doi: 10.1007/s00440-023-01235-3

  7. [14]

    Breden, H

    M. Breden, H. Chu, J. S. W. Lamb, and M. Rasmussen.Rigorous enclosure of Lyapunov exponents of stochastic flows. 2024. doi: 10.48550/arXiv.2411.07064. Pre-published

  8. [15]

    Computer-assisted proof of shear-induced chaos in stochastically perturbed Hopf systems

    M. Breden and M. Engel. “Computer-assisted proof of shear-induced chaos in stochastically perturbed Hopf systems”. In:The Annals of Applied Probability33.2 (Apr. 1, 2023).doi: 10.1214/22-AAP1841

  9. [16]

    Stochastic partial differential equations in M-type 2 Banach spaces

    Z. Brzeźniak. “Stochastic partial differential equations in M-type 2 Banach spaces”. In:Potential Analysis 4.1 (Feb. 1995), pp. 1–45.doi: 10.1007/BF01048965

  10. [17]

    The effect of noise on the Chafee–Infante equation: a nonlinear case study

    T. Caraballo, H. Crauel, J. Langa, and J. Robinson. “The effect of noise on the Chafee–Infante equation: a nonlinear case study”. In:Proceedings of the American Mathematical Society135.2 (Aug. 1, 2006), pp. 373–

  11. [18]

    The Lyapunov spectrum for conditioned random dynamical systems

    M. M. Castro, D. Chemnitz, H. Chu, M. Engel, J. S. W. Lamb, and M. Rasmussen. “The Lyapunov spectrum for conditioned random dynamical systems”. In:Annales de l’Institut Henri Poincaré, Probabilités et Statistiques(2022). In press

  12. [19]

    On the structure of attractors and invariant measures for a class of monotone random systems

    I. Chueshov and M. Scheutzow. “On the structure of attractors and invariant measures for a class of monotone random systems”. In:Dynamical Systems19.2 (June 2004), pp. 127–144.doi: 10.1080/1468936042000207792

  13. [20]

    An application of the generalized Morse index to travelling wave solutions of a competitive reaction-diffusion model

    C. Conley and R. Gardner. “An application of the generalized Morse index to travelling wave solutions of a competitive reaction-diffusion model”. In:Indiana University Mathematics Journal33.3 (1984), pp. 319–343

  14. [21]

    Additive noise destroys a pitchfork bifurcation

    H. Crauel and F. Flandoli. “Additive noise destroys a pitchfork bifurcation”. In:Journal of Dynamics and Differential Equations10.2 (1998), pp. 259–274.doi: 10.1023/A:1022665916629

  15. [22]

    Da Prato and J

    G. Da Prato and J. Zabczyk.Ergodicity for infinite dimensional systems. 1st ed. Cambridge University Press, May 16, 1996.doi: 10.1017/CBO9780511662829

  16. [23]

    Da Prato and J

    G. Da Prato and J. Zabczyk.Stochastic equations in infinite dimensions. Encyclopedia of Mathematics and its Applications. Cambridge University Press, 1992.doi: 10.1017/CBO9780511666223

  17. [24]

    Multiscale analysis for traveling-pulse solutions to the stochastic FitzHugh–Nagumo equations

    K. Eichinger, M. V. Gnann, and C. Kuehn. “Multiscale analysis for traveling-pulse solutions to the stochastic FitzHugh–Nagumo equations”. In:The Annals of Applied Probability32.5 (Oct. 1, 2022).doi: 10.1214/21- AAP1759

  18. [25]

    A random dynamical systems perspective on isochronicity for stochastic oscillations

    M. Engel and C. Kuehn. “A random dynamical systems perspective on isochronicity for stochastic oscillations”. In: Communications in Mathematical Physics386.3 (Sept. 2021), pp. 1603–1641.doi: 10.1007/s00220-021- 04077-z

  19. [26]

    Conditioned Lyapunov exponents for random dynamical systems

    M. Engel, J. S. W. Lamb, and M. Rasmussen. “Conditioned Lyapunov exponents for random dynamical systems”. In:Transactions of the American Mathematical Society372.9 (May 20, 2019), pp. 6343–6370.doi: 10.1090/tran/7803

  20. [27]

    The role of phase synchronization in memory processes

    J. Fell and N. Axmacher. “The role of phase synchronization in memory processes”. In:Nature Reviews Neuroscience 12.2 (Feb. 2011), pp. 105–118.doi: 10.1038/nrn2979

  21. [28]

    Existence of positive solutions to stochastic thin-film equations

    J. Fischer and G. Grün. “Existence of positive solutions to stochastic thin-film equations”. In:SIAM Journal on Mathematical Analysis50.1 (Jan. 2018), pp. 411–455.doi: 10.1137/16M1098796

  22. [30]

    Synchronization by noise for order-preserving random dynamical systems

    F. Flandoli, B. Gess, and M. Scheutzow. “Synchronization by noise for order-preserving random dynamical systems”. In:The Annals of Probability45.2 (Mar. 2017), pp. 1325–1350.doi: 10.1214/16-AOP1088

  23. [31]

    On the convergence of stochastic transport equations to a deterministic parabolic one

    L. Galeati. “On the convergence of stochastic transport equations to a deterministic parabolic one”. In: Stochastics and Partial Differential Equations: Analysis and Computations8.4 (Dec. 2020), pp. 833–868. doi: 10.1007/s40072-019-00162-6

  24. [32]

    Lyapunov exponents and synchronisation by noise for systems of SPDEs

    B. Gess and P. Tsatsoulis. “Lyapunov exponents and synchronisation by noise for systems of SPDEs”. In: The Annals of Probability52.5 (Sept. 1, 2024).doi: 10.1214/24-AOP1690

  25. [33]

    Small noise and long time phase diffusion in stochastic limit cycle oscillators

    G. Giacomin, C. Poquet, and A. Shapira. “Small noise and long time phase diffusion in stochastic limit cycle oscillators”. In:Journal of Differential Equations264.2 (Jan. 2018), pp. 1019–1049.doi: 10.1016/j.jde. 2017.09.029

  26. [34]

    M. V. Gnann, R. W. S. Westdorp, and J. van Winden.Solitary waves in a stochastic parametrically forced nonlinear Schrödinger equation. Mar. 7, 2024.doi: 10.48550/arXiv.2403.04625. Pre-published

  27. [35]

    Isochrons and phaseless sets

    J. Guckenheimer. “Isochrons and phaseless sets”. In:Journal of Mathematical Biology1.3 (Sept. 1975), pp. 259–273. doi: 10.1007/BF01273747

  28. [36]

    Homoclinic orbits of the FitzHugh–Nagumo equation: bifurcations in the full system

    J. Guckenheimer and C. Kuehn. “Homoclinic orbits of the FitzHugh–Nagumo equation: bifurcations in the full system”. In:SIAM Journal on Applied Dynamical Systems9.1 (Jan. 2010), pp. 138–153. doi: 10.1137/090758404

  29. [37]

    Ergodicity of the 2D Navier–Stokes equations with degenerate stochastic forcing

    M. Hairer and J. Mattingly. “Ergodicity of the 2D Navier–Stokes equations with degenerate stochastic forcing”. In:Annals of Mathematics164.3 (Nov. 1, 2006), pp. 993–1032.doi: 10.4007/annals.2006.164.993

  30. [38]

    Stability of traveling waves for reaction-diffusion equations with multiplicative noise

    C. H. S. Hamster and H. J. Hupkes. “Stability of traveling waves for reaction-diffusion equations with multiplicative noise”. In:SIAM Journal on Applied Dynamical Systems18.1 (Jan. 2019), pp. 205–278.doi: 10.1137/17M1159518. 26

  31. [39]

    Stability of traveling waves for systems of reaction-diffusion equations with multiplicative noise

    C. H. S. Hamster and H. J. Hupkes. “Stability of traveling waves for systems of reaction-diffusion equations with multiplicative noise”. In:SIAM Journal on Mathematical Analysis52.2 (Jan. 2020), pp. 1386–1426. doi: 10.1137/18M1226348

  32. [40]

    Travelling waves for reaction–diffusion equations forced by translation invariant noise

    C. H. S. Hamster and H. J. Hupkes. “Travelling waves for reaction–diffusion equations forced by translation invariant noise”. In:Physica D: Nonlinear Phenomena401 (Jan. 2020), p. 132233.doi: 10.1016/j.physd. 2019.132233

  33. [41]

    A general framework for stochastic traveling waves and patterns, with application to neural field equations

    J. Inglis and J. MacLaurin. “A general framework for stochastic traveling waves and patterns, with application to neural field equations”. In:SIAM Journal on Applied Dynamical Systems15.1 (Jan. 2016), pp. 195–234. doi: 10.1137/15M102856X

  34. [42]

    StabilityofthetravellingwavesolutionoftheFitzHugh–Nagumosystem

    C.K.R.T.Jones.“StabilityofthetravellingwavesolutionoftheFitzHugh–Nagumosystem”.In: Transactions of the American Mathematical Society286.2 (1984), pp. 431–469.doi: 10.2307/1999806

  35. [43]

    Jost.Partial differential equations

    J. Jost.Partial differential equations. Vol. 214. Graduate Texts in Mathematics. Springer New York, 2013. doi: 10.1007/978-1-4614-4809-9

  36. [44]

    Stochastic synchronization of neural activity waves

    Z. P. Kilpatrick. “Stochastic synchronization of neural activity waves”. In:Physical Review E91.4 (Apr. 16, 2015), p. 040701.doi: 10.1103/PhysRevE.91.040701

  37. [45]

    Front propagation in stochastic neural fields: a rigorous mathematical framework

    J. Krüger and W. Stannat. “Front propagation in stochastic neural fields: a rigorous mathematical framework”. In: SIAM Journal on Applied Dynamical Systems13.3 (Jan. 2014), pp. 1293–1310.doi: 10.1137/13095094X

  38. [46]

    Stochastic rotating waves

    C. Kuehn, J. MacLaurin, and G. Zucal. “Stochastic rotating waves”. In:Stochastics and Dynamics22.07 (Nov. 2022), p. 2240029.doi: 10.1142/S0219493722400299

  39. [47]

    Low-energy control of electrical turbulence in the heart

    S. Luther, F. H. Fenton, B. G. Kornreich, A. Squires, P. Bittihn, D. Hornung, M. Zabel, J. Flanders, A. Gladuli, L. Campoy, E. M. Cherry, G. Luther, G. Hasenfuss, V. I. Krinsky, A. Pumir, R. F. Gilmour, and E. Bodenschatz. “Low-energy control of electrical turbulence in the he...

  40. [48]

    Small random perturbations of dynamical systems: exponential loss of memory of the initial condition

    F. Martinelli and E. Scoppola. “Small random perturbations of dynamical systems: exponential loss of memory of the initial condition”. In:Communications in Mathematical Physics120.1 (Mar. 1988), pp. 25–69. doi: 10.1007/BF01223205

  41. [49]

    Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise

    J. Mattingly, A. Stuart, and D. Higham. “Ergodicity for SDEs and approximations: locally Lipschitz vector fields and degenerate noise”. In:Stochastic Processes and their Applications101.2 (Oct. 2002), pp. 185–232. doi: 10.1016/S0304-4149(02)00150-3

  42. [50]

    A simple proof of the support theorem for diffusion processes

    A. Millet and M. Sanz-Solé. “A simple proof of the support theorem for diffusion processes”. In:Séminaire de Probabilités XXVIII. Ed. by J. Azéma, M. Yor, and P. A. Meyer. Vol. 1583. Springer Berlin Heidelberg, 1994, pp. 36–48.doi: 10.1007/BFb0073832

  43. [51]

    Random travelling waves for the KPP equation with noise

    C. Mueller and R. Sowers. “Random travelling waves for the KPP equation with noise”. In:Journal of Functional Analysis128.2 (Mar. 1995), pp. 439–498.doi: 10.1006/jfan.1995.1038

  44. [52]

    Phase reduction approach to synchronisation of nonlinear oscillators

    H. Nakao. “Phase reduction approach to synchronisation of nonlinear oscillators”. In:Contemporary Physics 57.2 (Apr. 2, 2016), pp. 188–214.doi: 10.1080/00107514.2015.1094987

  45. [53]

    Synchrony of limit-cycle oscillators induced by random external impulses

    H. Nakao, K.-s. Arai, K. Nagai, Y. Tsubo, and Y. Kuramoto. “Synchrony of limit-cycle oscillators induced by random external impulses”. In:Physical Review E72.2 (Aug. 24, 2005), p. 026220.doi: 10.1103/PhysRevE. 72.026220

  46. [54]

    Noise-induced synchronization and clustering in ensembles of uncoupled limit-cycle oscillators

    H. Nakao, K. Arai, and Y. Kawamura. “Noise-induced synchronization and clustering in ensembles of uncoupled limit-cycle oscillators”. In:Physical Review Letters98.18 (May 2, 2007), p. 184101.doi: 10.1103/ PhysRevLett.98.184101

  47. [55]

    Effective long-time phase dynamics of limit- cycle oscillators driven by weak colored noise

    H. Nakao, J.-n. Teramae, D. S. Goldobin, and Y. Kuramoto. “Effective long-time phase dynamics of limit- cycle oscillators driven by weak colored noise”. In:Chaos: An Interdisciplinary Journal of Nonlinear Science 20.3 (Sept. 1, 2010), p. 033126.doi: 10.1063/1.3488977

  48. [56]

    Phase-reduction approach to synchronization of spatiotemporal rhythms in reaction-diffusion systems

    H. Nakao, T. Yanagita, and Y. Kawamura. “Phase-reduction approach to synchronization of spatiotemporal rhythms in reaction-diffusion systems”. In:Physical Review X4.2 (May 22, 2014), p. 021032.doi: 10.1103/ PhysRevX.4.021032

  49. [57]

    Necessary and sufficient conditions for stable synchronization in random dynamical systems

    J. Newman. “Necessary and sufficient conditions for stable synchronization in random dynamical systems”. In: Ergodic Theory and Dynamical Systems38.5 (Aug. 2018), pp. 1857–1875.doi: 10.1017/etds.2016.109

  50. [58]

    Synchronization and stochastization of array of self-excited oscillators by external noise

    A. S. Pikovskii. “Synchronization and stochastization of array of self-excited oscillators by external noise”. In: Radiophysics and Quantum Electronics27.5 (May 1984), pp. 390–395.doi: 10.1007/BF01044784

  51. [59]

    Phase synchronization in regular and chaotic systems

    A. Pikovsky, M. Rosenblum, and J. Kurths. “Phase synchronization in regular and chaotic systems”. In: International Journal of Bifurcation and Chaos10.10 (Oct. 2000), pp. 2291–2305. doi: 10 . 1142 / S0218127400001481

  52. [60]

    Pikovsky, M

    A. Pikovsky, M. Rosenblum, and J. Kurths.Synchronization: a universal concept in nonlinear sciences. 1st ed. Cambridge Nonlinear Science Series 12. Cambridge University Press, Oct. 18, 2001.doi: 10.1017/ CBO9780511755743

  53. [61]

    Phase synchronization of chaotic oscillators by external driving

    A. S. Pikovsky, M. G. Rosenblum, G. V. Osipov, and J. Kurths. “Phase synchronization of chaotic oscillators by external driving”. In:Physica D: Nonlinear Phenomena104.3-4 (June 1997), pp. 219–238.doi: 10.1016/S0167-2789(96)00301-6

  54. [62]

    Optimum bounds for the distributions of martingales in Banach spaces

    I. Pinelis. “Optimum bounds for the distributions of martingales in Banach spaces”. In:The Annals of Probability 22.4 (Oct. 1, 1994).doi: 10.1214/aop/1176988477. 27

  55. [63]

    Exponential estimates for stochastic convolutions in 2-smooth Banach spaces

    J. Seidler. “Exponential estimates for stochastic convolutions in 2-smooth Banach spaces”. In:Electronic Journal of Probability15 (Jan. 1, 2010).doi: 10.1214/EJP.v15-808

  56. [64]

    On the support of diffusion processes with applications to the strong maximum principle

    D. W. Stroock and S. R. S. Varadhan. “On the support of diffusion processes with applications to the strong maximum principle”. In:Proceedings of the Sixth Berkeley Symposium on Mathematical Statistics and Probability (Univ. California, Berkeley, Calif., 1970/1971), Vol. III: ...

  57. [65]

    Stochastic phase reduction for a general class of noisy limit cycle oscillators

    J.-n. Teramae, H. Nakao, and G. B. Ermentrout. “Stochastic phase reduction for a general class of noisy limit cycle oscillators”. In:Physical Review Letters102.19 (May 14, 2009), p. 194102.doi: 10.1103/PhysRevLett. 102.194102

  58. [66]

    Noise induced phase synchronization of a general class of limit cycle oscillators

    J.-n. Teramae and D. Tanaka. “Noise induced phase synchronization of a general class of limit cycle oscillators”. In: Progress of Theoretical Physics Supplement161 (2006), pp. 360–363.doi: 10.1143/PTPS.161.360

  59. [67]

    Robustness of the noise-induced phase synchronization in a general class of limit cycle oscillators

    J.-n. Teramae and D. Tanaka. “Robustness of the noise-induced phase synchronization in a general class of limit cycle oscillators”. In:Physical Review Letters93.20 (Nov. 12, 2004), p. 204103.doi: 10.1103/ PhysRevLett.93.204103

  60. [68]

    van den Bosch and H

    M. van den Bosch and H. J. Hupkes.Multidimensional stability of planar travelling waves for stochastically perturbed reaction-diffusion systems. June 6, 2024.doi: 10.48550/arXiv.2406.04232. Pre-published

  61. [69]

    van Winden.Noncommutative orbital stability of stochastic patterns in Banach spaces

    J. van Winden.Noncommutative orbital stability of stochastic patterns in Banach spaces. 2024. doi: 10. 48550/arXiv.2406.16642. Pre-published

  62. [70]

    On the approaching time towards the attractor of differential equations perturbed by small noise

    I. Vorkastner. “On the approaching time towards the attractor of differential equations perturbed by small noise”. In:Discrete & Continuous Dynamical Systems - B25.11 (2020), pp. 4295–4316.doi: 10.3934/dcdsb. 2020098

  63. [71]

    Coherence resonance and noise-induced synchronization in globally coupled Hodgkin–Huxley neurons

    Y. Wang, D. T. W. Chik, and Z. D. Wang. “Coherence resonance and noise-induced synchronization in globally coupled Hodgkin–Huxley neurons”. In:Physical Review E61.1 (Jan. 1, 2000), pp. 740–746.doi: 10.1103/PhysRevE.61.740

  64. [72]

    Long-timescale soliton dynamics in the Korteweg–de Vries equation with multiplicative translation-invariant noise

    R. W. S. Westdorp and H. J. Hupkes. “Long-timescale soliton dynamics in the Korteweg–de Vries equation with multiplicative translation-invariant noise”. In:Physica D: Nonlinear Phenomena460 (Apr. 2024), p. 134065. doi: 10.1016/j.physd.2024.134065

  65. [73]

    Patterns of phase compromise in biological cycles

    A. T. Winfree. “Patterns of phase compromise in biological cycles”. In:Journal of Mathematical Biology1.1 (May 1974), pp. 73–93.doi: 10.1007/BF02339491

  66. [74]

    Phase reduction of stochastic limit cycle oscillators

    K. Yoshimura and K. Arai. “Phase reduction of stochastic limit cycle oscillators”. In:Physical Review Letters 101.15 (Oct. 8, 2008), p. 154101.doi: 10.1103/PhysRevLett.101.154101. 28

  67. [382]

    doi: 10.1090/S0002-9939-06-08593-5

  68. [1998]

    doi: 10.1007/978-3-662-12878-7

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.