4.4 Article

Global solutions for 1D cubic defocusing dispersive equations: Part I

Journal

FORUM OF MATHEMATICS PI
Volume 11, Issue -, Pages -

Publisher

CAMBRIDGE UNIV PRESS
DOI: 10.1017/fmp.2023.30

Keywords

35B55; 35B40

Ask authors/readers for more resources

This article introduces a new nonperturbative method to prove global well-posedness and scattering for one-dimensional NLS problems with cubic nonlinearity. The method is based on a robust reinterpretation of the idea of Interaction Morawetz estimates, developed almost 20 years ago.
This article is devoted to a general class of one-dimensional NLS problems with a cubic nonlinearity. The question of obtaining scattering, global in time solutions for such problems has attracted a lot of attention in recent years, and many global well-posedness results have been proved for a number of models under the assumption that the initial data are both small and localized. However, except for the completely integrable case, no such results have been known for small but not necessarily localized initial data.In this article, we introduce a new, nonperturbative method to prove global well-posedness and scattering for $L<^>2$ initial data which are small and nonlocalized. Our main structural assumption is that our nonlinearity is defocusing. However, we do not assume that our problem has any exact conservation laws. Our method is based on a robust reinterpretation of the idea of Interaction Morawetz estimates, developed almost 20 years ago by the I-team.In terms of scattering, we prove that our global solutions satisfy both global $L<^>6$ Strichartz estimates and bilinear $L<^>2$ bounds. This is a Galilean invariant result, which is new even for the classical defocusing cubic NLS.1 There, by scaling, our result also admits a large data counterpart.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.4
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available