9 citations · 13 across the 14 of their papers we have counts for
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Uncertainty Quantification in Computational Fluid Dynamics: Physics and Machine Learning Based Approaches
Minghan Chu
Turbulent flow has been extensively studied using computational fluid dynamics (CFD) simulations since turbulent flow regime is so frequently encountered in both academic and engin…
Multi-fidelity Deep Learning-based methodology for epistemic uncertainty quantification of turbulence models
Minghan Chu, Weicheng Qian
Computational Fluid Dynamics (CFD) simulations using turbulence models are commonly used in engineering design. Of the different turbulence modeling approaches that are available,…
Uncertainty Quantification For Turbulent Flows with Machine Learning
Minghan Chu, Weicheng Qian
Turbulent flows are of central importance across applications in science and engineering problems. For design and analysis, scientists and engineers use Computational Fluid Dynamic…
Multi-Fidelity Data Assimilation For Physics Inspired Machine Learning In Uncertainty Quantification Of Fluid Turbulence Simulations
Minghan Chu, Weicheng Qian
Reliable prediction of turbulent flows is an important necessity across different fields of science and engineering. In Computational Fluid Dynamics (CFD) simulations, the most com…
Combination of Multi-Fidelity Data Sources For Uncertainty Quantification: A Lightweight CNN Approach
Minghan Chu, Weicheng Qian
Reynolds Averaged Navier Stokes (RANS) modelling is notorious for introducing the model-form uncertainty due to the Boussinesq turbulent viscosity hypothesis. Recently, the eigensp…