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 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…
A Deep Learning Approach For Epistemic Uncertainty Quantification Of Turbulent Flow Simulations
Minghan Chu, Weicheng Qian
Simulations of complex turbulent flow are part and parcel of the engineering design process. Eddy viscosity based turbulence models represent the workhorse for these simulations. T…
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…