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Random Data Wave Equations

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  • First Online:
Singular Random Dynamics

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Abstract

Nowadays we have many methods allowing to exploit the regularising properties of the linear part of a nonlinear dispersive equation (such as the KdV equation, the nonlinear wave or the nonlinear Schrödinger equations) in order to prove well-posedness in low regularity Sobolev spaces. By well-posedness in low regularity Sobolev spaces we mean that less regularity than the one imposed by the energy methods is required (the energy methods do not exploit the dispersive properties of the linear part of the equation). In many cases these methods to prove well-posedness in low regularity Sobolev spaces lead to optimal results in terms of the regularity of the initial data. By optimal we mean that if one requires slightly less regularity then the corresponding Cauchy problem becomes ill-posed in the Hadamard sense. We call the Sobolev spaces in which these ill-posedness results hold spaces of supercritical regularity. More recently, methods to prove probabilistic well-posedness in Sobolev spaces of supercritical regularity were developed. More precisely, by probabilistic well-posedness we mean that one endows the corresponding Sobolev space of supercritical regularity with a non degenerate probability measure and then one shows that almost surely with respect to this measure one can define a (unique) global flow. However, in most of the cases when the methods to prove probabilistic well-posedness apply, there is no information about the measure transported by the flow. Very recently, a method to prove that the transported measure is absolutely continuous with respect to the initial measure was developed. In such a situation, we have a measure which is quasi-invariant under the corresponding flow.

The aim of these lectures is to present all of the above described developments in the context of the nonlinear wave equation.

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Notes

  1. 1.

    Interestingly, variants of the Khinchin inequality will be essentially used in our probabilistic approach to the cubic defocusing wave equation with data of super-critical regularity.

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Acknowledgements

I am grateful to Leonardo Tolomeo, Tadahiro Oh and Yuzhao Wang for their remarks on the manuscript. I am very grateful to Chenmin Sun for pointing our an error in a previous version of Lemma 4.1.32. I am particularly indebted to Nicolas Burq and Tadahiro Oh since this text benefitted from the discussions we had on the topics discussed in the lectures. I am grateful to Franco Flandoli and Massimiliano Gubinelli for inviting me to give these lectures.

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Authors and Affiliations

  1. Department of Mathematics, Universite de Cergy-Pontoise, Cergy-Pontoise Cedex, France

    Nikolay Tzvetkov

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  1. Nikolay Tzvetkov
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Correspondence to Nikolay Tzvetkov .

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Editors and Affiliations

  1. Faculty of Sciences, Scuola Normale Superiore, Pisa, Italy

    Franco Flandoli

  2. Hausdorff Center for Mathematics, University of Bonn, Bonn, Germany

    Massimiliano Gubinelli

  3. Department of Mathematics, Imperial College London, London, UK

    Martin Hairer

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Tzvetkov, N. (2019). Random Data Wave Equations. In: Flandoli, F., Gubinelli, M., Hairer, M. (eds) Singular Random Dynamics . Lecture Notes in Mathematics(), vol 2253. Springer, Cham. https://doi.org/10.1007/978-3-030-29545-5_4

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