2 papers
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…
physics.flu-dyn2021
Generalizable Physics-constrained Modeling using Learning and Inference assisted by Feature Space Engineering
Vishal Srivastava, Karthik Duraisamy
This work presents a formalism to improve the predictive accuracy of physical models by learning generalizable augmentations from sparse data. Building on recent advances in data-d…