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Differentiable modeling to unify machine learning and physical models and advance Geosciences., , , , , , , , , and 19 other author(s). CoRR, (2023)Neural Ordinary Differential Equations for Data-Driven Reduced Order Modeling of Environmental Hydrodynamics., , and . AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)A decision making framework with MODFLOW-FMP2 via optimization: Determining trade-offs in crop selection., , , , , and . Environ. Model. Softw., (2015)Application of deep learning to large scale riverine flow velocity estimation., , , , , , and . CoRR, (2020)Preface., , , , and . AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Deep Learning-based Fast Solver of the Shallow Water Equations., , , , , , and . AAAI Spring Symposium: MLPS, volume 2964 of CEUR Workshop Proceedings, CEUR-WS.org, (2021)Application of Deep Learning-based Interpolation Methods to Nearshore Bathymetry., , , , , , and . CoRR, (2020)Surfzone Topography-informed Deep Learning Techniques to Nearshore Bathymetry with Sparse Measurements., , , , , and . AAAI Spring Symposium: MLPS, volume 2587 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)A 2D Fully Convolutional Neural Network for Nearshore And Surf-Zone Bathymetry Inversion from Synthetic Imagery of Surf-Zone using the Model Celeris., , , , , , and . AAAI Spring Symposium: MLPS, volume 2587 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)Data-driven reduced order modeling of environmental hydrodynamics using deep autoencoders and neural ODEs, , , , , and . (2021)cite arxiv:2107.02784Comment: 16 pages, 7 figures, To Appear in the proceedings of the IXth International Conference on Computational Methods for Coupled Problems in Science and Engineering (COUPLED PROBLEMS 2021), 14-16 June, 2021. arXiv admin note: substantial text overlap with arXiv:2104.13962.