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Adding Constraints to Bayesian Inverse Problems
Jiacheng Wu, Jian-Xun Wang, Shawn C. Shadden
Using observation data to estimate unknown parameters in computational models is broadly important. This task is often challenging because solutions are non-unique due to the compl…
Prediction of Reynolds Stresses in High-Mach-Number Turbulent Boundary Layers using Physics-Informed Machine Learning
Jian-Xun Wang, Junji Huang, Lian Duan +1
Modeled Reynolds stress is a major source of model-form uncertainties in Reynolds-averaged Navier-Stokes (RANS) simulations. Recently, a physics-informed machine-learning (PIML) ap…
Data-augmented modeling of intracranial pressure
Jian-Xun Wang, Xiao Hu, Shawn C. Shadden
Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ab…
Physics-Informed Machine Learning Approach for Augmenting Turbulence Models: A Comprehensive Framework
Jin-Long Wu, Heng Xiao, Eric Paterson
Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncert…