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Found 4 papers, 3 papers with code
Deep learning-based fast solver of the shallow water equations
Furthermore, we augment the bathymetry posterior distribution to a more general class of distributions before providing them as inputs to ML algorithm in the second stage.
Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields.
Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields.
Application of deep learning to large scale riverine flow velocity estimation
First, using the principal component geostatistical approach (PCGA) we estimate the probability density function of the bathymetry from flow velocity measurements, and then we use multiple machine learning algorithms to obtain a fast solver of the SWEs, given augmented realizations from the posterior bathymetry distribution and the prescribed range of BCs.
