Uncertainty quantification for data-driven turbulence modelling with Mondrian forests
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Publication:2124898
DOI10.1016/j.jcp.2021.110116OpenAlexW3009434468MaRDI QIDQ2124898
Pranay Seshadri, Ashley Scillitoe, Mark A. Girolami
Publication date: 11 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2003.01968
turbulence modellinguncertainty quantificationdataset shiftsupervised machine learningmachine learning interpretabilityMondrian forests
Uses Software
Cites Work
- LES of heat transfer in electronics
- Data-driven modelling of the Reynolds stress tensor using random forests with invariance
- Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks
- A random matrix approach for quantifying model-form uncertainties in turbulence modeling
- A paradigm for data-driven predictive modeling using field inversion and machine learning
- Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches
- Explicit algebraic Reynolds stress and non-linear eddy-viscosity models
- Turbulent Flows
- Highly resolved large-eddy simulation of separated flow in a channel with streamwise periodic constrictions
- Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned
- Reynolds averaged turbulence modelling using deep neural networks with embedded invariance
- Turbulence Modeling in the Age of Data
- Random forests
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