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cs.CE2022
A Non-intrusive Approach for Physics-constrained Learning with Application to Fuel Cell Modeling
Vishal Srivastava, Valentin Sulzer, Peyman Mohtat +2
A data-driven model augmentation framework, referred to as Weakly-coupled Integrated Inference and Machine Learning (IIML), is presented to improve the predictive accuracy of physi…
cs.CE2016
Machine Learning-augmented Predictive Modeling of Turbulent Separated Flows over Airfoils
Anand Pratap Singh, Shivaji Medida, Karthik Duraisamy
A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our app…