| 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 | 153 | 2020 |
| First principles and machine learning virtual flow metering: a literature review T Bikmukhametov, J Jäschke Journal of Petroleum Science and Engineering 184, 106487, 2020 | 140 | 2020 |
| Oil production monitoring using gradient boosting machine learning algorithm T Bikmukhametov, J Jäschke Ifac-Papersonline 52 (1), 514-519, 2019 | 69 | 2019 |
| 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 | 16 | 2022 |
| CFD Simulations of multiphase flows with particles T Bikmukhametov NTNU, 2016 | 12 | 2016 |
| 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, D031S022R004, 2018 | 3 | 2018 |
| 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 | 2 | 2022 |
| Machine Learning and First Principles Modeling Applied to Multiphase Flow Estimation T Bikmukhametov NTNU, 2020 | 1 | 2020 |
| Upstream Oil and Gas Technology MG Alfarizi, M Stanko, T Bikmukhametov | | |
Johannes JäschkeProfessor, Department Chemical Engineering, Norwegian University of Science andVerifisert e-postadresse på ntnu.no
