A data-assimilation method for Reynolds-averaged Navier–Stokes-driven mean flow reconstruction

By D. P. G. Foures, N. Dovetta, Denis Sipp, Peter J. Schmid. Journal of Fluid Mechanics, 2014

152 citations recorded by the source corpus.

Subfields: Computational Mechanics, Ocean Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6d006ff126d4875dcb94dcec26a0b8769fe858ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Jian-Xun Wang
2. Luning Sun
3. Han Gao
4. Yang Liu
5. Chengping Rao
6. Matthew J. Zahr

In Ocean Engineering

1. Luning Sun
2. Jian-Xun Wang

In Statistical and Nonlinear Physics

1. Jian-Xun Wang
2. Luning Sun
3. Han Gao
4. Yang Liu
5. Chengping Rao
6. Matthew J. Zahr

Citations in the corpus

• Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data (2020, methodology): “The sparse measurement data can be fused into the model-based predictions using data assimilation (DA) techniques, e.g., ensemble Kalman filter, particle filters, or variational based DA techniques [16–21].”