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Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs
Reduced order modeling of time-dependent problems using deep autoencoders and neural ordinary differential equations. Numerical comparisons are presented with several other model order reduction methodologies.
Sourav Dutta, Peter Rivera-Casillas, Orie Cecil, Matthew Farthing, Emma Perracchione, Mario Putti
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Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics
Reduced order modeling of time dependent PDEs using proper orthogonal decomposition and neural ordinary differental equations.
Sourav Dutta, Peter Rivera-Casillas, Matthew Farthing
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Optimization of a Managed Aquifer Recharge Network
Over the last few decades, groundwater resources in many regions have been depleted at a faster rate than the underlying aquifers have …
J.C. Chrispell, Matthew Farthing, K.R. Fowler, Stacy E. Howington, Eleanor Jenkins, Sourav Dutta, Bingbing Ji
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