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Artificial Intelligence for Modeling and Simulation (AIMS) Lab

RESEARCH BLOG

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

Aug 13, 20241 min read

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

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

Aug 13, 20241 min read

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

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

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

Aug 13, 20241 min read

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

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

Learning About Structural Errors in Models of Complex Dynamical Systems

Aug 13, 20242 min read

Learning About Structural Errors in Models of Complex Dynamical Systems

Learning About Structural Errors in Models of Complex Dynamical Systems

Causation Entropy Boosting

Aug 13, 20242 min read

Causation Entropy Boosting

Causation Entropy Boosting

Learning Stochastic Closures Using Ensemble Kalman Inversion

Oct 29, 20212 min read

Learning Stochastic Closures Using Ensemble Kalman Inversion

Learning Stochastic Closures Using Ensemble Kalman Inversion

Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions

Oct 29, 20212 min read

Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions

Epidemic Management and Control Through Risk-Dependent Individual Contact Interventions

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

Oct 29, 20211 min read

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

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

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical

Oct 29, 20212 min read

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical

Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical

Physics-informed machine learning approach for augmenting turbulence models

Nov 8, 20182 min read

Physics-informed machine learning approach for augmenting turbulence models

Physics-informed machine learning approach for augmenting turbulence models

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

Nov 8, 20181 min read

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

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

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

Nov 8, 20182 min read

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

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

Seeing permeability from images: fast prediction with convolutional neural networks

Nov 2, 20181 min read

Seeing permeability from images: fast prediction with convolutional neural networks

Seeing permeability from images: fast prediction with convolutional neural networks

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