Artificial Intelligence for Modeling and Simulation (AIMS) Lab

Data-Driven Stochastic Closure Modeling via Conditional Diffusion Model and Neural Operator

Conditional Gaussian Neural Stochastic Differential Equation for Modeling Complex Systems and Data Assimilation

Neural Dynamical Operator with Gradient-Based and Derivative-Free Optimization Methods

Learning About Structural Errors in Models of Complex Dynamical Systems

Causation Entropy Boosting

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