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Timur Bikmukhametov
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Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models
T Bikmukhametov, J Jäschke
Computers & Chemical Engineering 138, 106834, 2020
672020
First principles and machine learning virtual flow metering: a literature review
T Bikmukhametov, J Jäschke
Journal of Petroleum Science and Engineering 184, 106487, 2020
602020
Oil production monitoring using gradient boosting machine learning algorithm
T Bikmukhametov, J Jäschke
Ifac-Papersonline 52 (1), 514-519, 2019
322019
CFD Simulations of multiphase flows with particles
T Bikmukhametov
NTNU, 2016
82016
Statistical analysis of effect of sensor degradation and heat transfer modeling on multiphase flowrate estimates from a virtual flow meter
T Bikmukhametov, M Stanko, J Jäschke
SPE Asia Pacific Oil and Gas Conference and Exhibition, 2018
22018
Well control optimization in waterflooding using genetic algorithm coupled with Artificial Neural Networks
MG Alfarizi, M Stanko, T Bikmukhametov
Upstream Oil and Gas Technology 9, 100071, 2022
12022
Machine Learning and First Principles Modeling Applied to Multiphase Flow Estimation
T Bikmukhametov
NTNU, 2020
12020
Hybrid Machine Learning Modeling of Engineering Systems--A Probabilistic Perspective Tested on a Multiphase Flow Modeling Case Study
T Bikmukhametov, J Jäschke
arXiv preprint arXiv:2205.09196, 2022
2022
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Artikler 1–8