| Machine learning methods for herschel–bulkley fluids in annulus: Pressure drop predictions and algorithm performance evaluation A Kumar, S Ridha, T Ganet, P Vasant, SU Ilyas Applied Sciences 10 (7), 2588, 2020 | 25 | 2020 |
| Rheological behavior of stabilized diamond-graphene nanoplatelets hybrid nanosuspensions in mineral oil SU Ilyas, S Ridha, S Sardar, P Estellé, A Kumar, R Pendyala Journal of Molecular Liquids 328, 115509, 2021 | 23 | 2021 |
| Physics-guided deep neural network to characterize non-Newtonian fluid flow for optimal use of energy resources A Kumar, S Ridha, M Narahari, SU Ilyas Expert Systems with Applications 183, 115409, 2021 | 17 | 2021 |
| Application of boundary-fitted convolutional neural network to simulate non-Newtonian fluid flow behavior in eccentric annulus A Kumar, S Ridha, SU Ilyas, I Dzulkarnain, A Pratama Neural Computing and Applications 34 (14), 12043-12061, 2022 | 4 | 2022 |
| Application of Machine Learning Algorithms in Predicting Rheological Behavior of BN-diamond/Thermal Oil Hybrid Nanofluids A Ali, N Noshad, A Kumar, SU Ilyas, PE Phelan, M Alsaady, R Nasir, ... Fluids 9 (1), 20, 2024 | | 2024 |
| Unsupervised Deep Learning Algorithm to Solve Sub-Surface Dynamics for Petroleum Engineering Applications A Kumar, S Ridha, SU Ilyas 2020 International Conference on Computational Intelligence (ICCI), 98-102, 2020 | | 2020 |
| Sensitivity Analysis of Low Salinity Waterflood Alternating Co2 Injection (Co2-Lswag) Performance Using Machine Learning Application in Sandstone Reservoir MR Efras, I Dzulkarnain, S Ridha, LA Syahputra, K Abhishek, M Yusuf, ... Available at SSRN 4181438, 0 | | |
Suhaib Umer IlyasUniversity of Jeddah, Universiti Teknologi PETRONASVerified email at uj.edu.sa
