| Deep learning forecasts a strained turbulent flow velocity field in temporal Lagrangian framework: Comparison of LSTM and GRU R Hassanian, Á Helgadóttir, M Riedel Fluids 7 (11), 344, 2022 | 11 | 2022 |
| Practice and experience in using parallel and scalable machine learning with heterogenous modular supercomputing architectures M Riedel, R Sedona, C Barakat, P Einarsson, R Hassanian, G Cavallaro, ... 2021 IEEE International Parallel and Distributed Processing Symposium …, 2021 | 11 | 2021 |
| An experiment generates a specified mean strained rate turbulent flow: Dynamics of particles R Hassanian, A Helgadottir, L Bouhlali, M Riedel Physics of Fluids 35 (1), 2023 | 9 | 2023 |
| The capability of recurrent neural networks to predict turbulence flow via spatiotemporal features R Hassanian, M Riedel, L Bouhlali 2022 IEEE 10th Jubilee International Conference on Computational Cybernetics …, 2022 | 9 | 2022 |
| Implicit Equation for Photovoltaic Module Temperature and Efficiency via Heat Transfer Computational Model R Hassanian, M Riedel, A Helgadottir, N Yeganeh, R Unnthorsson Thermo 2022 2 (1), 39-55, 2022 | 6 | 2022 |
| A practical approach for estimating the optimum tilt angle of a photovoltaic panel for a long period—experimental recorded data R Hassanian, M Riedel, N Yeganeh, R Unnthorsson Solar 1 (1), 41-51, 2021 | 6* | 2021 |
| Leading-edge erosion and floating particles: Stagnation point simulation in particle-laden turbulent flow via Lagrangian particle tracking R Hassanian, M Riedel Machines 11 (5), 566, 2023 | 5 | 2023 |
| Numerical Investigation on the Acceleration Vibration Response of Linear Actuator R Hassanian, M Riedel, N Yeganeh Open Access Library Journal 9 (4), 1-7, 2022 | 5 | 2022 |
| Deciphering the dynamics of distorted turbulent flows: Lagrangian particle tracking and chaos prediction through transformer-based deep learning models R Hassanian, H Myneni, A Helgadóttir, M Riedel Physics of Fluids 35 (7), 2023 | 4 | 2023 |
| Iceland wind farm assessment case study and development: An empirical data from wind and wind turbine R Hassanian, Á Helgadóttir, M Riedel Cleaner Energy Systems 4, 100058, 2023 | 4 | 2023 |
| A proposed hybrid two-stage dl-hpc method for wind speed forecasting: using the first average forecast output for long-term forecasting R Hassanian, A Helgadóttir, M Aach, A Lintermann, M Riedel Proceedings of the IACM Computational Fluids Conference, CFC, 2023 | 3 | 2023 |
| Vertical axis wind turbine powers telecom towers: Green and clean configuration R Hassanian, H Myneni, Á Helgadóttir, M Riedel 2023 6th International Conference on Electrical Engineering and Green Energy …, 2023 | 2 | 2023 |
| Lagrangian particle tracking data of a straining turbulent flow assessed using machine learning and parallel computing R Hassanian, M Riedel, A Helgadottir, P Costa, L Bouhlali ParCFD2022, 2022 | 2 | 2022 |
| A Review in Context to Wind Effect on NOCT Model for Photovoltaic Panel R Hassanian, M Riedel, N Yeganeh Peer Review Journal of Solar & Photoenergy Systems 2 (1), 2022 | 2 | 2022 |
| Wind Velocity and Forced Heat Transfer Model for Photovoltaic Module R Hassanian, N Yeganeh, M Riedel Fluids 9 (1), 17, 2024 | 1 | 2024 |
| A wake loss model asymmetry induced by the circulation of a vertical axis wind turbine R Hassanian, Á Helgadóttir, M Riedel 2023 International Conference on Future Energy Solutions (FES), 1-5, 2023 | 1 | 2023 |
| A Novel Implicit Model Determines the Photovoltaic Panel Temperature and Environmental Effects R Hassanian, M Riedel, N Yeganeh, Á Helgadòttir APS March Meeting Abstracts 2023, D45. 013, 2023 | 1 | 2023 |
| Mechanical Elements Analysis of Stewart Platform: Computational Approach R Hassanian, M Riedel International Journal of Science and Research (IJSR) 11 (4), 1166 - 1171, 2022 | 1 | 2022 |
| Turbulent Flow Prediction-Simulation: Strained Flow with Initial Isotropic Condition Using a GRU Model Trained by an Experimental Lagrangian Framework, with Emphasis on … R Hassanian, M Aach, A Lintermann, Á Helgadóttir, M Riedel Fluids 9 (4), 84, 2024 | | 2024 |
| Optimizing Wind Energy Production: Leveraging Deep Learning Models Informed with On-Site Data and Assessing Scalability through HPC R Hassanian, A Shahinfar, Á Helgadóttir, M Riedel Acta Polytechnica Hungarica 21 (9), 2024 | | 2024 |
Prof. Dr.-Ing. Morris RiedelProfessor of High Performance Computing & Machine Learning, Forschungszentrum Juelich & UoIcelandVerifierad e-postadress på fz-juelich.de
