Space-dependent turbulence model aggregation using machine learning

DOI10.1016/j.jcp.2023.112628arXiv2301.09013OpenAlexW4388672845MaRDI QIDQ6119255

No author found.

Publication date: 29 February 2024

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2301.09013

zbMATH Keywords

turbulent flowsRANS equationsRANS modelsmodel mixtureNACA65space-dependent weights

Mathematics Subject Classification ID

Parametric inference (62Fxx) Turbulence (76Fxx) Artificial intelligence (68Txx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bagging predictors
• Bayesian estimates of parameter variability in the \(k-\varepsilon\) turbulence model
• Predictive RANS simulations via Bayesian model-scenario averaging
• Clustered Bayesian model averaging
• The weighted majority algorithm
• Bayesian model averaging: A tutorial. (with comments and a rejoinder).
• Epistemic uncertainties in RANS model free coefficients
• Boosting the margin: a new explanation for the effectiveness of voting methods
• Support-vector networks
• Forecasting electricity consumption by aggregating specialized experts
• Sequential model aggregation for production forecasting
• Bayesian model-scenario averaged predictions of compressor cascade flows under uncertain turbulence models
• A paradigm for data-driven predictive modeling using field inversion and machine learning
• Direct numerical simulations of transition in a compressor cascade: the influence of free-stream turbulence
• An explicit algebraic Reynolds stress model for incompressible and compressible turbulent flows
• Turbulent Flows
• Optimal sensor placement for variational data assimilation of unsteady flows past a rotationally oscillating cylinder
• How to use expert advice
• A Fast Learning Algorithm for Deep Belief Nets
• Random forests

This page was built for publication: Space-dependent turbulence model aggregation using machine learning