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Found 6 papers, 5 papers with code
Extreme Event Prediction with Multi-agent Reinforcement Learning-based Parametrization of Atmospheric and Oceanic Turbulence
Reinforcement learning is emerging as a potent alternative for developing such closures as it requires only low-order statistics and leads to stable closures.
Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
These closures depend on nonlinear combinations of gradients of filtered variables, with constants that are independent of the fluid/flow properties and only depend on filter type/size.
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks
A parallel architecture with two branches is proposed.
Discovery of interpretable structural model errors by combining Bayesian sparse regression and data assimilation: A chaotic Kuramoto-Sivashinsky test case
Models of many engineering and natural systems are imperfect.
Physics-aware registration based auto-encoder for convection dominated PDEs
We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arising from convection-dominated nonlinear physical systems.
Lagrangian basis method for dimensionality reduction of convection dominated nonlinear flows
Foundations of a new projection-based model reduction approach for convection dominated nonlinear fluid flows are summarized.
Fluid Dynamics
