A service-oriented architecture for coupling web service models using the Basic Model Interface (BMI) P Jiang, M Elag, P Kumar, SD Peckham, L Marini, L Rui Environmental Modelling & Software 92, 107-118, 2017 | 63 | 2017 |
Debates—Does information theory provide a new paradigm for Earth science? Causality, interaction, and feedback AE Goodwell, P Jiang, BL Ruddell, P Kumar Water Resources Research 56 (2), e2019WR024940, 2020 | 60 | 2020 |
Explore spatio‐temporal learning of large sample hydrology using graph neural networks AY Sun, P Jiang, MK Mudunuru, X Chen Water Resources Research 57 (12), e2021WR030394, 2021 | 43 | 2021 |
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion AY Sun, P Jiang, ZL Yang, Y Xie, X Chen Hydrology and Earth System Sciences 26 (19), 5163-5184, 2022 | 33 | 2022 |
Digital Twin Earth--Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators P Jiang, N Meinert, H Jordão, C Weisser, S Holgate, A Lavin, B Lütjens, ... 2021 NeurIPS Workshop on Machine Learning for the Physical Sciences (ML4PS), 2021 | 33 | 2021 |
Information transfer from causal history in complex system dynamics P Jiang, P Kumar Physical Review E 99 (1), 012306, 2019 | 26 | 2019 |
Estimating watershed subsurface permeability from stream discharge data using deep neural networks E Cromwell, P Shuai, P Jiang, ET Coon, SL Painter, JD Moulton, Y Lin, ... Frontiers in Earth Science 9, 613011, 2021 | 18 | 2021 |
Using ensemble data assimilation to estimate transient hydrologic exchange flow under highly dynamic flow conditions K Chen, X Chen, X Song, MA Briggs, P Jiang, P Shuai, G Hammond, ... Water Resources Research 58 (5), e2021WR030735, 2022 | 16 | 2022 |
Establishing rainfall depth–duration–frequency relationships at daily raingauge stations in Hong Kong P Jiang, YK Tung Journal of hydrology 504, 80-93, 2013 | 16 | 2013 |
Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado P Jiang, P Shuai, A Sun, MK Mudunuru, X Chen Hydrology and Earth System Sciences 27 (14), 2621-2644, 2023 | 14 | 2023 |
Using information flow for whole system understanding from component dynamics P Jiang, P Kumar Water Resources Research 55 (11), 8305-8329, 2019 | 14 | 2019 |
Interactions of information transfer along separable causal paths P Jiang, P Kumar Physical Review E 97 (4), 042310, 2018 | 13 | 2018 |
Efficient Super‐Resolution of Near‐Surface Climate Modeling Using the Fourier Neural Operator P Jiang, Z Yang, J Wang, C Huang, P Xue, TC Chakraborty, X Chen, ... Journal of Advances in Modeling Earth Systems 15 (7), e2023MS003800, 2023 | 8 | 2023 |
Incorporating daily rainfalls to derive rainfall DDF relationships at ungauged sites in Hong Kong and quantifying their uncertainty P Jiang, YK Tung Stochastic environmental research and risk assessment 29, 45-62, 2015 | 8 | 2015 |
DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters P Jiang, X Chen, K Chen, J Anderson, N Collins, MEL Gharamti Environmental Modelling & Software 142, 105074, 2021 | 7 | 2021 |
SWAT watershed model calibration using deep learning MK Mudunuru, K Son, P Jiang, X Chen arXiv preprint arXiv:2110.03097, 2021 | 6 | 2021 |
Bundled causal history interaction P Jiang, P Kumar Entropy 22 (3), 360, 2020 | 6 | 2020 |
Scalable deep learning for watershed model calibration MK Mudunuru, K Son, P Jiang, G Hammond, X Chen Frontiers in Earth Science 10, 1026479, 2022 | 4 | 2022 |
Using Mutual Information for Global Sensitivity Analysis on Watershed Modeling P Jiang, K Son, MK Mudunuru, X Chen Water Resources Research, e2022WR032932, 2022 | 4 | 2022 |
EdgeAI: How to use AI to collect reliable and relevant watershed data MK Mudunuru, X Chen, S Karra, G Hammond, P Jiang, KC Solander, ... Artificial Intelligence for Earth System Predictability (AI4ESP …, 2021 | 3 | 2021 |