Kuai Fang

Generating author description...

All published works

Action	Title	Year	Authors
+	Differentiable modelling to unify machine learning and physical models for geosciences	2023	Chaopeng Shen Alison Appling Pierre Gentine Toshiyuki Bandai Hoshin V. Gupta Alexandre M. Tartakovsky Marco Baity‐Jesi Fabrizio Fenicia Daniel Kifer Li Li
+	Differentiable modeling to unify machine learning and physical models and advance Geosciences	2023	Chaopeng Shen Alison Appling Pierre Gentine Toshiyuki Bandai Hoshin V. Gupta Alexandre M. Tartakovsky Marco Baity‐Jesi Fabrizio Fenicia Daniel Kifer Yuanyuan Li
+ PDF Chat	The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology	2022	Kuai Fang Daniel Kifer Kathryn Lawson Dapeng Feng Chaopeng Shen
+ PDF Chat	Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short‐Term Memory Models for Soil Moisture Predictions	2020	Kuai Fang Daniel Kifer Kathryn Lawson Chaopeng Shen
+ PDF Chat	Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales	2020	Dapeng Feng Kuai Fang Chaopeng Shen
+	Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network	2017	Kuai Fang Chaopeng Shen Daniel Kifer Xiao Yang

Common Coauthors

Coauthor	Papers Together
Chaopeng Shen	6
Daniel Kifer	5
Kathryn Lawson	4
Dapeng Feng	3
Matthew W. Farthing	2
Doaa Aboelyazeed	2
Chonggang Xu	2
Marvin Höge	2
Toshiyuki Bandai	2
C. J. Harman	2
Hoshin V. Gupta	2
Tadd Bindas	2
Hylke E. Beck	2
Alexandre M. Tartakovsky	2
Binayak P. Mohanty	2
Pierre Gentine	2
Alison Appling	2
Dipankar Dwivedi	2
Tirthankar Roy	2
Marco Baity‐Jesi	2
Fabrizio Fenicia	2
Martyn Clark	2
Wei Ren	2
Christopher Rackauckas	2
Yalan Song	1
Xiaofeng Liu	1
Xiaofeng Liu	1
Xiao Yang	1
Farshid Rahmani	1
Yuanyuan Li	1
Li Li	1
Dapeng Feng	1
Praveen Kumar	1
Praveen Kumar	1
Farshid Rahmani	1
Yi Zheng	1
Yi Zheng	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network	2017	Kuai Fang Chaopeng Shen Daniel Kifer Xiao Yang	4
+ PDF Chat	A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists	2018	Chaopeng Shen	3
+	Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data	2017	Anuj Karpatne Gowtham Atluri James H. Faghmous Michael Steinbach Arindam Banerjee Auroop R. Ganguly Shashi Shekhar Nagiza Samatova Vipin Kumar	2
+ PDF Chat	Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets	2019	Frederik Kratzert Daniel Klotz Guy Shalev Günter Klambauer Sepp Hochreiter Grey Nearing	2
+ PDF Chat	Deep learning in neural networks: An overview	2014	Jürgen Schmidhuber	2
+ PDF Chat	Enhancing Streamflow Forecast and Extracting Insights Using Long‐Short Term Memory Networks With Data Integration at Continental Scales	2020	Dapeng Feng Kuai Fang Chaopeng Shen	2
+ PDF Chat	Distributed long-term hourly streamflow predictions using deep learning – A case study for State of Iowa	2020	Zhongrun Xiang İbrahim Demir	2
+ PDF Chat	Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks	2018	Frederik Kratzert Daniel Klotz Claire Brenner Karsten Schulz Mathew Herrnegger	2
+ PDF Chat	From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling	2021	Wen‐Ping Tsai Dapeng Feng Ming Pan Hylke E. Beck Kathryn Lawson Yuan Yang Jiangtao Liu Chaopeng Shen	2
+ PDF Chat	Evaluating the Potential and Challenges of an Uncertainty Quantification Method for Long Short‐Term Memory Models for Soil Moisture Predictions	2020	Kuai Fang Daniel Kifer Kathryn Lawson Chaopeng Shen	2
+	ADADELTA: An Adaptive Learning Rate Method	2012	Matthew D. Zeiler	2
+	What Role Does Hydrological Science Play in the Age of Machine Learning?	2020	Grey Nearing Frederik Kratzert Alden Keefe Sampson Craig Pelissier Daniel Klotz Jonathan Frame Cristina Prieto Hoshin V. Gupta	2
+	Reconciling modern machine-learning practice and the classical bias–variance trade-off	2019	Mikhail Belkin Daniel Hsu Siyuan Ma Soumik Mandal	1
+	Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm	2016	Qiang Liu Dilin Wang	1
+	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	2015	Yarin Gal Zoubin Ghahramani	1
+ PDF Chat	Deep learning to represent subgrid processes in climate models	2018	Stephan Rasp Michael S. Pritchard Pierre Gentine	1
+ PDF Chat	Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles	2019	Xiaowei Jia Jared Willard Anuj Karpatne Jordan S. Read Jacob A. Zwart Michael Steinbach Vipin Kumar	1
+ PDF Chat	The proper care and feeding of CAMELS: How limited training data affects streamflow prediction	2020	Martin Gauch Juliane Mai Jimmy Lin	1
+ PDF Chat	Deep learning of subsurface flow via theory-guided neural network	2020	Nanzhe Wang Dongxiao Zhang Haibin Chang Heng Li	1
+ PDF Chat	DeepXDE: A Deep Learning Library for Solving Differential Equations	2021	Lu Lu Xuhui Meng Zhiping Mao George Em Karniadakis	1
+ PDF Chat	AI Feynman: A physics-inspired method for symbolic regression	2020	Silviu‐Marian Udrescu Max Tegmark	1
+ PDF Chat	Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport	2020	Qizhi He David A. Barajas‐Solano G. Tartakovsky Alexandre M. Tartakovsky	1
+ PDF Chat	Learning constitutive relations from indirect observations using deep neural networks	2020	Daniel Zhengyu Huang Kailai Xu Charbel Farhat Eric Darve	1
+ PDF Chat	A comprehensive review of deep learning applications in hydrology and water resources	2020	Muhammed Sit Bekir Demiray Zhongrun Xiang Gregory J. Ewing Yusuf Sermet İbrahim Demir	1
+ PDF Chat	On doing large-scale hydrology with Lions: Realising the value of perceptual models and knowledge accumulation	2020	Thorsten Wagener Tom Gleeson Gemma Coxon Andreas Hartmann Nicholas Howden Francesca Pianosi Shams Rahman Rafael Rosolem Lina Stein Ross Woods	1
+ PDF Chat	Improving Seasonal Forecast Using Probabilistic Deep Learning	2022	Baoxiang Pan Gemma J. Anderson André Gonçalves D. D. Lucas C. Bonfils Jiwoo Lee	1
+ PDF Chat	Stable architectures for deep neural networks	2017	Eldad Haber Lars Ruthotto	1
+	Deep Convolutional Encoder‐Decoder Networks for Uncertainty Quantification of Dynamic Multiphase Flow in Heterogeneous Media	2018	Shaoxing Mo Yinhao Zhu Nicholas Zabaras Xiaoqing Shi Jichun Wu	1
+ PDF Chat	Rainfall–runoff prediction at multiple timescales with a single Long Short-Term Memory network	2021	Martin Gauch Frederik Kratzert Daniel Klotz Grey Nearing Jimmy Lin Sepp Hochreiter	1
+	Physics guided machine learning using simplified theories	2021	Suraj Pawar Omer San Burak Aksoylu Adil Rasheed Trond Kvamsdal	1
+ PDF Chat	The Data Synergy Effects of Time‐Series Deep Learning Models in Hydrology	2022	Kuai Fang Daniel Kifer Kathryn Lawson Dapeng Feng Chaopeng Shen	1
+	Hybrid FEM-NN models: Combining artificial neural networks with the finite element method	2021	Sebastian K. Mitusch Simon W. Funke Miroslav Kuchta	1
+ PDF Chat	Application of deep learning to large scale riverine flow velocity estimation	2021	Mojtaba Forghani Yizhou Qian Jonghyun Lee Matthew W. Farthing Tyler Hesser Peter K. Kitanidis Eric Darve	1
+	Machine learning–accelerated computational fluid dynamics	2021	Dmitrii Kochkov Jamie Smith Ayya Alieva Mengqing Wang Michael P. Brenner Stephan Hoyer	1
+ PDF Chat	Continental-scale streamflow modeling of basins with reservoirs: Towards a coherent deep-learning-based strategy	2021	Wenyu Ouyang Kathryn Lawson Dapeng Feng Lei Ye Chi Zhang Chaopeng Shen	1
+ PDF Chat	Three ways to solve partial differential equations with neural networks — A review	2021	Jan Blechschmidt Oliver G. Ernst	1
+ PDF Chat	Mitigating Prediction Error of Deep Learning Streamflow Models in Large Data‐Sparse Regions With Ensemble Modeling and Soft Data	2021	Dapeng Feng Kathryn Lawson Chaopeng Shen	1
+ PDF Chat	Variational encoder geostatistical analysis (VEGAS) with an application to large scale riverine bathymetry	2022	Mojtaba Forghani Yizhou Qian Jonghyun Lee Matthew W. Farthing Tyler Hesser Peter K. Kitanidis Eric Darve	1
+ PDF Chat	Causality for Machine Learning	2022	Bernhard Schölkopf	1
+	Continuous Deep Equilibrium Models: Training Neural ODEs faster by integrating them to Infinity	2022	Avik Pal Alan Edelman Christopher Rackauckas	1
+ PDF Chat	Why we need to focus on developing ethical, responsible, and trustworthy artificial intelligence approaches for environmental science	2022	Amy McGovern Imme Ebert‐Uphoff David John Gagne Ann Bostrom	1
+	Bathymetry Inversion using a Deep-Learning-Based Surrogate for Shallow Water Equations Solvers	2022	Xiaofeng Liu Yalan Song Chaopeng Shen	1
+	Priors for Infinite Networks	1996	Radford M. Neal	1
+	Machine Learning for Understanding Inland Water Quantity, Quality, and Ecology	2022	Alison Appling Samantha K. Oliver Jordan S. Read Jeffrey M. Sadler Jacob A. Zwart	1
+ PDF Chat	On the principles of Parsimony and Self-consistency for the emergence of intelligence	2022	Yi Ma Doris Y. Tsao Heung‐Yeung Shum	1
+	Universal Differential Equations for Scientific Machine Learning	2020	Christopher Rackauckas Yingbo Ma Carl Julius Martensen Collin Warner Kirill Zubov Rohit Supekar Dominic J. Skinner Ali J. Ramadhan Alan Edelman	1
+	DeepClouds.ai: Deep learning enabled computationally cheap direct numerical simulations	2022	Moumita Bhowmik Manmeet Mahinderjit Singh Suryachandra A. Rao Souvik Paul	1
+ PDF Chat	Differentiable, Learnable, Regionalized Process‐Based Models With Multiphysical Outputs can Approach State‐Of‐The‐Art Hydrologic Prediction Accuracy	2022	Dapeng Feng Jiangtao Liu Kathryn Lawson Chaopeng Shen	1
+	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	2015	Yarin Gal Zoubin Ghahramani	1
+ PDF Chat	Distributed long-term hourly streamflow predictions using deep learning – A case study for State of Iowa	2020	Zhongrun Xiang İbrahim Demir	1