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06/23/2023
Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil
Recently, physics informed neural networks (PINNs) have been explored ex...
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03/18/2023
Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensive...
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08/02/2022
Reduced-order modeling for parameterized large-eddy simulations of atmospheric pollutant dispersion
Mapping near-field pollutant concentration is essential to track acciden...
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06/09/2021
Observation data compression for variational assimilation of dynamical systems
Accurate estimation of error covariances (both background and observatio...
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03/05/2021
Physics-aware deep neural networks for surrogate modeling of turbulent natural convection
Recent works have explored the potential of machine learning as data-dri...
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01/31/2020
A graph clustering approach to localization for adaptive covariance tuning in data assimilation based on state-observation mapping
An original graph clustering approach to efficient localization of error...
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10/11/2019
