4 papers
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…
Machine learning based uncertainty quantification of turbulence model for airfoils
Minghan Chu, Weicheng Qian
Reynolds-averaged Navier-Stokes (RANS)-based transition modeling is widely used in aerospace applications but suffers inaccuracies due to the Boussinesq turbulent viscosity hypothe…
Multi-Scale Simulation Modeling for Prevention and Public Health Management of Diabetes in Pregnancy and Sequelae
Yang Qin, Louise Freebairn, Jo-An Atkinson +3
Diabetes in pregnancy (DIP) is an increasing public health priority in the Australian Capital Territory, particularly due to its impact on risk for developing Type 2 diabetes. Whil…