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A Comparative Study of Machine Learning Models for Predicting the State
  of Reactive Mixing

A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing

Journal of Computational Physics (JCP), 2020
24 February 2020
B. Ahmmed
M. Mudunuru
S. Karra
S. James
V. Vesselinov
ArXiv (abs)PDFHTML

Papers citing "A Comparative Study of Machine Learning Models for Predicting the State of Reactive Mixing"

3 / 3 papers shown
Learning the Factors Controlling Mineralization for Geologic Carbon
  Sequestration
Learning the Factors Controlling Mineralization for Geologic Carbon Sequestration
Aleksandra Pachalieva
J. Hyman
Daniel O’Malley
Hari S. Viswanathan
G. Srinivasan
175
0
0
20 Dec 2023
A deep learning modeling framework to capture mixing patterns in
  reactive-transport systems
A deep learning modeling framework to capture mixing patterns in reactive-transport systemsCommunications in Computational Physics (Commun. Comput. Phys.), 2021
N. V. Jagtap
M. Mudunuru
K. Nakshatrala
135
5
0
11 Jan 2021
Physics-Informed Machine Learning Models for Predicting the Progress of
  Reactive-Mixing
Physics-Informed Machine Learning Models for Predicting the Progress of Reactive-MixingComputer Methods in Applied Mechanics and Engineering (CMAME), 2019
M. Mudunuru
S. Karra
172
12
0
28 Aug 2019
1
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