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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 Processes

22 April 2021
Pravin Bhasme
Jenil Vagadiya
Udit Bhatia
    AI4CE
ArXivPDFHTML

Papers citing "Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes"

3 / 3 papers shown
Title
A Review of Physics-based Machine Learning in Civil Engineering
A Review of Physics-based Machine Learning in Civil Engineering
S. Vadyala
S. N. Betgeri
J. Matthews
Elizabeth Matthews
AI4CE
25
152
0
09 Oct 2021
Integrating Scientific Knowledge with Machine Learning for Engineering
  and Environmental Systems
Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems
J. Willard
X. Jia
Shaoming Xu
M. Steinbach
Vipin Kumar
AI4CE
91
389
0
10 Mar 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,156
0
06 Jun 2015
1