| Differentiable modelling to unify machine learning and physical models for geosciences C Shen, AP Appling, P Gentine, T Bandai, H Gupta, A Tartakovsky, ... Nature Reviews Earth & Environment 4 (8), 552-567, 2023 | 61* | 2023 |
| Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning T Bindas, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian, K Lawson, C Shen Water Resources Research 60 (1), e2023WR035337, 2024 | 16* | 2024 |
| Routing flood waves through the river network utilizing physics-guided machine learning and the Muskingum-Cunge Method T Bindas, C Shen, Y Bian AGU Fall Meeting Abstracts 2020, H224-04, 2020 | 2 | 2020 |
| Differentiable modeling for global water resources under global change C Shen, Y Song, F Rahmani, T Bindas, D Aboelyazeed, K Sawadekar, ... EGU24, 2024 | | 2024 |
| Enhanced Continental Runoff Prediction through Differentiable Muskingum-Cunge Routing (δMC-CONUS-hydroDL2) T Bindas, Y Song, J Rapp, K Lawson, C Shen EGU24, 2024 | | 2024 |
| Can Attention Models Surpass LSTM in Hydrology? J Liu, C Shen, T Bindas EGU24, 2024 | | 2024 |
| Expanding Differentiable Muskingum-Cunge River Routing: Moving from the catchment-scale to continental-scale T Bindas, D Feng, J Liu, J Rapp, Y Bian, C Shen AGU23, 2023 | | 2023 |
| Value of Hydrofabric Artifact Static Parameters for Deep Learning Next Generation National Water Model (NextGen) Development J Rapp, JM Frame, R Araki, T Bindas, SA Bhuiyan AGU23, 2023 | | 2023 |
| Enhancing the Conceptual Functional Equivalent (CFE) rainfall-runoff model via a differentiable modeling approach R Araki, T Bindas, SA Bhuiyan, J Rapp, HK McMillan, FL Ogden, ... AGU23, 2023 | | 2023 |
| dpLGAR: a differentiable parameter learning implementation of the Layered Green & Ampt with redistribution (LGAR) model T Bindas, P La Follette, A Jan, FL Ogden, J Rapp, R Araki, SA Bhuiyan, ... AGU23, 2023 | | 2023 |
| On the spontaneous synchronization of hydrologic processes and hydrologic modeling JM Frame, T Bindas, R Araki, J Rapp, SA Bhuiyan AGU23, 2023 | | 2023 |
| Representing soil physical processes in Conceptual Framework Equivalent (CFE) through the implementation of Ordinary Differential Equation (ODE) SA Bhuiyan, R Araki, T Bindas, J Rapp, HK McMillan, FL Ogden, ... AGU23, 2023 | | 2023 |
| Differentiable modeling for global hydrology C Shen, D Feng, Y Song, D Aboelyazeed, F Rahmani, T Bindas AGU23, 2023 | | 2023 |
| Deep learning insights into suspended sediment concentrations across the conterminous United States: Strengths and limitations Y Song, P Chaemchuen, F Rahmani, W Zhi, L Li, X Liu, E Boyer, T Bindas, ... EGU General Assembly Conference Abstracts, EGU-16821, 2023 | | 2023 |
| Improving Large-Basin Streamflow Simulation Using a Differentiable, Learnable Routing Model T Bindas, C Shen, WP Tsai, J Liu, F Rahmani, D Feng, Y Bian AGU Fall Meeting Abstracts 2022, H45L-1539, 2022 | | 2022 |
| How to beat your teachers in hydrologic machine learning C Shen, J Liu, WP Tsai, T Bindas, F Rahmani, K Lawson AGU Fall Meeting Abstracts 2021, H31H-07, 2021 | | 2021 |
| Discovering Localized River Parameters via Physics-Guided Machine Learning and the Muskingum-Cunge Method T Bindas, C Shen, WP Tsai, Y Bian AGU Fall Meeting Abstracts 2021, H35S-1255, 2021 | | 2021 |
| Remote optical sensor with optical fiber for brake condition monitoring M Kane, T Newman, R Mulhern, T Bindas US Patent 11,174,910, 2021 | | 2021 |
| Road Salts and Faults: Evidence for Preferential Transport of High Salinity Groundwater via Geologic Structures that Connect Highways to Streams PZ Klos, T Bindas AGU Fall Meeting Abstracts 2019, H21L-1918, 2019 | | 2019 |
| Synchronization in hydrologic processes and modeling the response with concepts, physics and neural networks JM Frame, T Bindas, R Araki, J Rapp, E Deardorff | | |
Chaopeng ShenAssociate Professor in Civil Engineering, Pennsylvania State UniversityVerified email at engr.psu.edu
