Artificial Intelligence for Modeling and Simulation (AIMS) Lab

Learning Stochastic Closures Using Ensemble Kalman Inversion

Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions

Ensemble Kalman Inversion for Sparse Learning of Dynamical Systems from Time-Averaged Data

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical

Physics-informed machine learning approach for augmenting turbulence models

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies

Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations

Seeing permeability from images: fast prediction with convolutional neural networks