7 papers
Physics-Guided Machine Learning for Uncertainty Quantification in Turbulence Models
Minghan Chu, Weicheng Qian
Predicting the evolution of turbulent flows is central across science and engineering. Most studies rely on simulations with turbulence models, whose empirical simplifications intr…
Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Turbulence Models represent the workhorse for simulations used in engineering design and analysis. Despite their low computational cost and robustness, these models suffer from sub…
Convolutional Neural Networks For Turbulent Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Complex turbulent flow simulations are an integral aspect of the engineering design process. The mainstay of these simulations is represented by eddy viscosity based turbulence mod…
Physics Based & Machine Learning Methods For Uncertainty Estimation In Turbulence Modeling
Minghan Chu
Turbulent flows play an important role in many scientific and technological design problems. Both Sub-Grid Scale (SGS) models in Large Eddy Simulations (LES) and Reynolds Averaged…
Physics Constrained Deep Learning For Turbulence Model Uncertainty Quantification
Minghan Chu, Weicheng Qian
Engineering design and scientific analysis rely upon computer simulations of turbulent fluid flows using turbulence models. These turbulence models are empirical and approximate, l…
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…