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Physics Guided Machine Learning Methods for Hydrology
v1v2 (latest)

Physics Guided Machine Learning Methods for Hydrology

2 December 2020
A. Khandelwal
Shaoming Xu
Xiang Li
X. Jia
M. Steinbach
C. Duffy
John L. Nieber
Vipin Kumar
    AI4CE
ArXiv (abs)PDFHTML

Papers citing "Physics Guided Machine Learning Methods for Hydrology"

15 / 15 papers shown
A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM
A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM
Sudan Pokharel
Tirthankar Roy
127
12
0
11 Apr 2024
Causal hybrid modeling with double machine learning
Causal hybrid modeling with double machine learning
Kai-Hendrik Cohrs
Gherardo Varando
Nuno Carvalhais
Markus Reichstein
Gustau Camps-Valls
509
20
0
20 Feb 2024
FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
Shiyuan Luo
Juntong Ni
Shengyu Chen
Runlong Yu
Yiqun Xie
Licheng Liu
Zhenong Jin
Huaxiu Yao
Xiaowei Jia
714
10
0
17 Nov 2023
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for
  Machine Learning and Process-based Hydrology
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Qingsong Xu
Yilei Shi
Jonathan Bamber
Ye Tuo
Ralf Ludwig
Xiao Xiang Zhu
AI4CE
947
22
0
08 Oct 2023
Time Series Predictions in Unmonitored Sites: A Survey of Machine
  Learning Techniques in Water Resources
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water ResourcesEnvironmental Data Science (EDS), 2023
J. Willard
C. Varadharajan
X. Jia
Vipin Kumar
AI4TS
329
26
0
18 Aug 2023
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal
  Dynamics and Test-Time Refinement
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
Shengyu Chen
Tianshu Bao
P. Givi
Can Zheng
Xiaowei Jia
AI4CE
347
0
0
24 Apr 2023
Physics-informed neural networks for solving forward and inverse
  problems in complex beam systems
Physics-informed neural networks for solving forward and inverse problems in complex beam systemsIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Taniya Kapoor
Hongrui Wang
A. Núñez
R. Dollevoet
AI4CEPINN
328
96
0
02 Mar 2023
Differentiable modeling to unify machine learning and physical models
  and advance Geosciences
Differentiable modeling to unify machine learning and physical models and advance Geosciences
Chaopeng Shen
A. Appling
Pierre Gentine
Toshiyuki Bandai
H. Gupta
...
Chris Rackauckas
Tirthankar Roy
Chonggang Xu
Binayak Mohanty
K. Lawson
AI4CE
389
19
0
10 Jan 2023
Modeling Reservoir Release Using Pseudo-Prospective Learning and
  Physical Simulations to Predict Water Temperature
Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water TemperatureSDM (SDM), 2022
X. Jia
Shengyu Chen
Yiqun Xie
Haoyu Yang
A. Appling
S. Oliver
Zhe Jiang
DiffM
189
3
0
11 Feb 2022
A Review of Physics-based Machine Learning in Civil Engineering
A Review of Physics-based Machine Learning in Civil EngineeringResults in Engineering (RE), 2021
S. Vadyala
S. N. Betgeri
J. Matthews
Elizabeth Matthews
AI4CE
358
205
0
09 Oct 2021
SWAT Watershed Model Calibration using Deep Learning
SWAT Watershed Model Calibration using Deep Learning
M. Mudunuru
K. Son
Pin Jiang
X. Chen
243
4
0
06 Oct 2021
Robust Inverse Framework using Knowledge-guided Self-Supervised
  Learning: An application to Hydrology
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Rahul Ghosh
Arvind Renganathan
Kshitij Tayal
Xiang Li
A. Khandelwal
X. Jia
C. Duffy
J. Neiber
Vipin Kumar
262
26
0
14 Sep 2021
AdjointNet: Constraining machine learning models with physics-based
  codes
AdjointNet: Constraining machine learning models with physics-based codes
S. Karra
B. Ahmmed
M. Mudunuru
AI4CEPINNOOD
208
4
0
08 Sep 2021
Domain-guided Machine Learning for Remotely Sensed In-Season Crop Growth
  Estimation
Domain-guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation
G. Worrall
Anand Rangarajan
J. Judge
302
11
0
24 Jun 2021
Enhancing predictive skills in physically-consistent way: Physics
  Informed Machine Learning for Hydrological Processes
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological ProcessesJournal of Hydrology (J. Hydrol.), 2021
Pravin Bhasme
Jenil Vagadiya
Udit Bhatia
AI4CE
98
106
0
22 Apr 2021
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