Combining direct and indirect sparse data for learning generalizable turbulence models
From MaRDI portal
Publication:6107115
DOI10.1016/j.jcp.2023.112272arXiv2305.14759MaRDI QIDQ6107115
Guo-wei He, Heng Xiao, Xiao-Dong Luo, Xin-Lei Zhang
Publication date: 3 July 2023
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2305.14759
Cites Work
- Unnamed Item
- Flow over periodic hills -- numerical and experimental study in a wide range of Reynolds numbers
- Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: a data-driven, physics-informed Bayesian approach
- 4D large scale variational data assimilation of a turbulent flow with a dynamics error model
- Regularized ensemble Kalman methods for inverse problems
- A novel evolutionary algorithm applied to algebraic modifications of the RANS stress-strain relationship
- Ensemble Kalman inversion for sparse learning of dynamical systems from time-averaged data
- Multi-objective CFD-driven development of coupled turbulence closure models
- Evaluation of ensemble methods for quantifying uncertainties in steady-state CFD applications with small ensemble sizes
- Assimilation of disparate data for enhanced reconstruction of turbulent mean flows
- Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Physics-informed covariance kernel for model-form uncertainty quantification with application to turbulent flows
- Reconstruction of unsteady viscous flows using data assimilation schemes
- Conditioning and accurate solutions of Reynolds average Navier–Stokes equations with data-driven turbulence closures
- Analysis of the Ensemble and Polynomial Chaos Kalman Filters in Bayesian Inverse Problems
- A more general effective-viscosity hypothesis
- On predicting the turbulence-induced secondary flows using nonlinear k-ε models
- Turbulent Flows
- Ensemble Kalman method for learning turbulence models from indirect observation data
- Ensemble Gradient for Learning Turbulence Models from Indirect Observations
- DAFI: An Open-Source Framework for Ensemble-Based Data Assimilation and Field Inversion
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
- Analysis of the Ensemble Kalman Filter for Inverse Problems
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Turbulence Modeling in the Age of Data
- Hybrid iterative ensemble smoother for history matching of hierarchical models
This page was built for publication: Combining direct and indirect sparse data for learning generalizable turbulence models