466 citations · 526 across the 8 of their papers we have counts for
8 papers
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen, Alison P. Appling, Pierre Gentine +27
Process-Based Modeling (PBM) and Machine Learning (ML) are often perceived as distinct paradigms in the geosciences. Here we present differentiable geoscientific modeling as a powe…
Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Estimation of riverbed profiles, also known as bathymetry, plays a vital role in many applications, such as safe and efficient inland navigation, prediction of bank erosion, land s…
Deep learning-based fast solver of the shallow water equations
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Fast and reliable prediction of river flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used for t…
Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs
Sourav Dutta, Peter Rivera-Casillas, Orie M. Cecil +3
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. In a previous work [arXiv:2104.13962], we explored…
Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics
Sourav Dutta, Peter Rivera-Casillas, Matthew W. Farthing
Model reduction for fluid flow simulation continues to be of great interest across a number of scientific and engineering fields. Here, we explore the use of Neural Ordinary Differ…
Application of deep learning to large scale riverine flow velocity estimation
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee +4
Fast and reliable prediction of riverine flow velocities is important in many applications, including flood risk management. The shallow water equations (SWEs) are commonly used fo…
Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry
Yizhou Qian, Mojtaba Forghani, Jonghyun Harry Lee +4
Nearshore bathymetry, the topography of the ocean floor in coastal zones, is vital for predicting the surf zone hydrodynamics and for route planning to avoid subsurface features. H…
A greedy non-intrusive reduced order model for shallow water equations
Sourav Dutta, Matthew W. Farthing, Emma Perracchione +2
In this work, we develop Non-Intrusive Reduced Order Models (NIROMs) that combine Proper Orthogonal Decomposition (POD) with a Radial Basis Function (RBF) interpolation method to c…