Parameter estimation for the stochastically perturbed Navier-Stokes equations

By Igor Cialenco, Nathan E. Glatt-Holtz. 2010

52 citations recorded by the source corpus.

We consider a parameter estimation problem of determining the viscosity [nu] of a stochastically perturbed 2D Navier-Stokes system. We derive several different classes of estimators based on the first N Fourier modes of a single sample path observed on a finite time interval. We study the consistency and asymptotic normality of these estimators. Our analysis treats strong, pathwise solutions for both the periodic and bounded domain cases in the presence of an additive white (in time) noise.

Subfields: Computational Theory and Mathematics, Control and Systems Engineering, Finance

View Paper: https://www.semanticscholar.org/paper/a8c54c77bcb27f947decc5a2d282b62fea8b1c0e

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In Computational Theory and Mathematics

1. Markus Bibinger
2. Mathias Trabs

In Control and Systems Engineering

1. Markus Bibinger
2. Mathias Trabs

In Finance

1. Mathias Trabs
2. Markus Bibinger
3. Florian Hildebrandt

Citations in the corpus

• Volatility estimation for stochastic PDEs using high-frequency observations (2017, methodology): “Bishwal (2002) discusses a Bayesian approach and Cialenco et al. (2016) a trajectory fitting procedure. Hübner and Lototsky (2000) and Prakasa Rao (2002) have studied nonparametric estimators when the Fourier coefficients are observed continuously or in discrete time, respectively. We refer to the s”