Solving the 4NLS with white noise initial data

Tadahiro Oh, Nikolay Tzvetkov, Yuzhao Wang

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We construct global-in-time singular dynamics for the (renormalized) cubic fourth-order nonlinear Schrödinger equation on the circle, having the white noise measure as an invariant measure. For this purpose, we introduce the ‘random-resonant / nonlinear decomposition’, which allows us to single out the singular component of the solution. Unlike the classical McKean, Bourgain, Da Prato-Debussche type argument, this singular component is nonlinear, consisting of arbitrarily high powers of the random initial data. We also employ a random gauge transform, leading to random Fourier restriction norm spaces. For this problem, a contraction argument does not work, and we instead establish the convergence of smooth approximating solutions by studying the partially iterated Duhamel formulation under the random gauge transform. We reduce the crucial nonlinear estimates to boundedness properties of certain random multilinear functionals of the white noise.

Original languageEnglish
Article numbere48
JournalForum of Mathematics, Sigma
Publication statusAccepted/In press - 2020

Bibliographical note

Publisher Copyright:
© The Author(s) 2020. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.


  • Biharmonic nonlinear Schrrödinger equation
  • Fourth-order nonlinear Schrrödinger equation
  • Invariant measure
  • Random Fourier restriction norm space
  • Random-resonant/nonlinear decomposition
  • White noise

ASJC Scopus subject areas

  • Analysis
  • Theoretical Computer Science
  • Algebra and Number Theory
  • Statistics and Probability
  • Mathematical Physics
  • Geometry and Topology
  • Discrete Mathematics and Combinatorics
  • Computational Mathematics


Dive into the research topics of 'Solving the 4NLS with white noise initial data'. Together they form a unique fingerprint.

Cite this