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Publications

Q2/2024

Garcia Amboage, J. P., Wulff, E., Girone, M. & Pena, T.F. 2024. "Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing", EPJ Web of Conferences 295, 12005 (2024)
DOI: https://doi.org/10.1051/epjconf/202429512005

Q2/2024

Serhani, A., Xing, V., Dupuy, D., Lapeyre, C. & Staffelbach, G. 2024. "Graph and convolutional neural network coupling with a high-performance large-eddy simulation solver" ScienceDirekt, May 16 2024, 106306
DOI: https://doi.org/10.1016/j.compfluid.2024.106306

Q2/2024

Hassanian, R., Shahinfar A., Helgadóttir Á. & Riedel, M.. 2024. " Optimizing Wind Energy Production: Leveraging Deep Learning Models Informed with On-Site Data and Assessing Scalability through HPC" Acta Polytechnica Hungarica, no. 21: 9, 2024
DOI: https://doi.org/10.12700/APH.21.9.2024.9.4

Q2/2024

Hassanian, R., Aach M., Lintermann A., Helgadóttir Á. & Riedel M.. 2024. " Turbulent Flow Prediction-Simulation: Strained flow with Initial Isotropic Condition Using a GRU Model Trained by an Experimental Lagrangian Framework, with Emphasis on Hyperparameter Optimization." Fluids 9, no. 4: 84, 2024
DOI: https://doi.org/10.3390/fluids9040084

Q2/2024

Pata, J., Wulff, E., Mokhtar, F., Southwick, D., Zhang, M., Girone, M., & Duarte, J. (2024). Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors. Communications Physics, 7(1), 124.
DOI: https://doi.org/10.1038/s42005-024-01599-5

Q2/2024

Rüttgers, M., Waldmann, M., Vogt, K., Ilgner, J., Schröder, W., & Lintermann, A. (2024). Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning. Computers in Biology and Medicine, 173, 108383.
DOI: https://doi.org/10.1016/j.compbiomed.2024.108383

Q2/2024

Hassanian, R., Aach, M., Lintermann, A., Helgadóttir, Á., & Riedel, M. (2024). Turbulent Flow Prediction-Simulation: Strained Flow with Initial Isotropic Condition Using a GRU Model Trained by an Experimental Lagrangian Framework, with Emphasis on Hyperparameter Optimization. Fluids, 9(4, Special Issue Turbulent Flow, 2nd Edition), 84.
DOI: https://doi.org/10.3390/fluids9040084

Q1/2024

Demou, A. D., & Savva, N. (2024). Hybrid AI-Analytical Modeling of Droplet Dynamics on Inclined Heterogeneous Surfaces. Mathematics, 12(8).
DOI: https://doi.org/10.3390/math12081188

Q1/2024

Hassanian, R., Yeganeh, N., & Riedel, M. 2024. "Wind Velocity and Forced Heat Transfer Model for Photovoltaic Module" Fluids 9, no. 1: 17, 2024
DOI: https://doi.org/10.3390/fluids9010017

Q4/2023

Aach, M., Sarma, R., Inanc, E., Riedel, M., & Lintermann, A. (2023). Short Paper: Accelerating Hyperparameter Optimization Algorithms with Mixed Precision. Proceedings of the SC ’23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis, 1776–1779.
DOI: https://doi.org/10.1145/3624062.3624259

Q4/2023

Hassanian, R. & Riedel, M. 2023. "Buckling Assessment in the Dynamics Mechanisms, Stewart Platform Case Study: In the Context of Loads and Joints, Deflection Positions Gradient" Computation 11, no. 11: 227, 2023
DOI: https://doi.org/10.3390/computation11110227

Q4/2023

Hassanian, R., Helgadottir, A., Velte, C. & Riedel, M. “Turbulent flow prediction: Lagrangian Particle Tracking-Deep Learning (LPT-DL) based models”, 76th Annual Meeting of the Division of Fluid Dynamics 2023, APS, Bulletin of the DFD2023 American Physical Society, November 19–21, 2023; Washington, DC
DOI: https://meetings.aps.org/Meeting/DFD23/Session/X43.7

Q4/2023

Cavallaro, G., Sedona, R., Riedel, M., Lintermann, A., & Michielsen, K. (2023). "Challenges and Opportunities in the Adoption of High Performance Computing for Earth Observation in the Exascale Era". In P. Soille, S. Lumnitz, & S. Albani (Eds.), Proceedings of the 2023 conference on Big Data from Space (BiDS’23) - From foresight to impact (pp. 25–28). Publications Office of the European Union
DOI: https://doi.org/10.2760/46796

Q4/2023

Demou, A. & Savva, N., “AI-assisted modeling of capillary-driven droplet dynamics”, Cambridge University Press, October 2023
DOI: https://doi.org/10.1017/dce.2023.19

Q3/2023

R. Hassanian, H. Myneni, Á. Helgadóttir & M. Riedel, “Deciphering the dynamics of distorted turbulent flows: Lagrangian particle tracking and chaos prediction through transformer-based deep learning models”, Physics of Fluids Volume 35, Issue 7, July 2023
DOI: https://doi.org/10.1063/5.0157897

Q3/2023

Blanc, C., Ahar, A. & De Grave, K., “Reference dataset and benchmark for reconstructing laser parameters from on-axis video in powder bed fusion of bulk stainless steel”, Additive Manufacturing Letters, Volume 7, December 2023, 100161
DOI: https://doi.org/10.1016/j.addlet.2023.100161

Q2/2023

Hassanian, R. & Riedel, M. Leading-Edge Erosion and Floating Particles: Stagnation Point Simulation in Particle-Laden Turbulent Flow via Lagrangian Particle Tracking, Machines 11, no. 5: 566, 2023
DOI: https://doi.org/10.3390/machines11050566

Q2/2023

Hassanian, R., Helgadottir, A. & Riedel, M. “A wake loss model asymmetry induced by the circulation of a vertical axis wind turbine” IEEE International Conference on Future Energy Solutions, FES2023, June 12-14: Vaasa, Finland
DOI: https://doi.org/10.1109/fes57669.2023.10182719

Q2/2023

Hassanian R, Myneni H, Helgadottir A, Riedel M. “Vertical Axis Wind Turbine Powers Telecom Towers: Green and Clean Configuration”, IEEE The 6th International Conference on Electrical Engineering and Green Energy, CEEGE2023, June 6-9: Grimstad, Norway, 2023, pp. 114-118
DOI: https://doi.org/10.1109/CEEGE58447.2023.10246601

Q2/2023

M. Riedel; C. Barakat; S. Fritsch; M. Aach; J. Busch; A. Lintermann; A. Schuppert; S. Brynjólfsson; H. Neukirchen & M. Book, “Enabling Hyperparameter-Tuning of AI Models for Healthcare using the CoE RAISE Unique AI Framework for HPC”, 2023 46th MIPRO ICT and Electronics Convention (MIPRO).
DOI: https://doi.org/10.1016/j.addlet.2023.100161

Q2/2023

Hassanian, R. (Corresponding author), Helgadottir, A., Aach, M., Lintermann, A. & Riedel, M., “A proposed hybrid two-stage DL-HPC method for wind speed forecasting: using the first average forecast output for long-term forecasting”, Proceedings of the IACM Computational Fluids Conference (CFC2023).
JuSER (Open Access)

Q2/2023

Aach, M. (Corresponding author), Wulff, E., Pasetto, E., Delilbasic, A., Sarma, R., Inanc, E., Girone, M., Riedel, M. & Lintermann, A., “A Hybrid Quantum-Classical Workflow for Hyperparameter Optimization of Neural Networks”, ISC High Performance 2023, ISC2023.
JuSER (Open Access)

Q2/2023

Aach, M., Inanc, E., Sarma, R., Riedel, M. & Lintermann, A. “Large scale performance analysis of distributed deep learning frameworks for convolutional neural networks”, J Big Data 10, 96 (2023).
DOI: https://doi.org/10.1186/s40537-023-00765-w | JuSER (Open Access)

Q1/2023

Hassanian, R., Yeganeh, N., Helgadottir, A. & Riedel, M. “A Novel Implicit Model Determines the Photovoltaic Panel Temperature and Environmental Effects”, APS March Meeting 2023, Bulletin of the American Physical Society, March 5-10, Las Vegas, Nevada, 2023.
APS March Meeting (Open Access)

Q1/2023

Inanc, E. (Corresponding author), Sarma, R., Aach, M. & Lintermann, A., “AI4HPC v0.1”, zenodo.
DOI: http://dx.doi.org/10.5281/ZENODO.7705421 | JuSER (Open Access)

Q1/2023

Albers, M., Meysonnat, P. S., Fernex, D., Semaan, R., Noack, B. R., Schröder, W. & Lintermann, A. (Corresponding author), “Actuated Turbulent Boundary Layer Flows Dataset”, EUDAT - B2SHARE
DOI: http://dx.doi.org/10.34730/5dbc8e35f21241d0889906136cf28d26 | JuSER (Open Access)

Q1/2023

Barakat, C. (Corresponding author), Aach, M., Schuppert, A., Brynjólfsson, S., Fritsch, S. & Riedel, M., “Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support”, Diagnostics 2023, 13(3), 391
DOI: http://dx.doi.org/10.3390/diagnostics13030391 | JuSER (Open Access)

Q1/2023

Hassanian R., Helgadottir A., L Bouhlali L., Riedel M. “An Experiment Generates a Specified Mean Strained Rate Turbulent Flow: Dynamics of Particles”, Physics of Fluids, Vol. 35(1), 2023.
DOI: https://doi.org/10.1063/5.0134306

Q1/2023

Orland, F., Brose, K. S., Bissantz, J., Ferraro, F., Terboven, C. & Hasse, C. (2023). A Case Study on Coupling OpenFOAM with Different Machine Learning Frameworks.
DOI: 10.1109/AI4S56813.2022.00007

Q4/2022

Hassanian, R., Helgadottir, A. & Riedel, M. “Parallel computing accelerates sequential deep networks model in turbulent flow forecasting”. The International Conference for High Performance Computing, Networking, Storage, and Analysis, SC22, Dallas, November 13-18, Dallas, 2022.
SC22 Supercomputing (Open Access)

Q4/2022

Hassanian, R., Helgadottir, A. & Riedel, M. “Deep Learning Forecasts a Strained Turbulent Flow Velocity Field in Temporal Lagrangian Framework: Comparison of LSTM and GRU”, Fluids, 2022, 7(11), 344.
DOI: https://doi.org/10.3390/fluids7110344

Q4/2022

Gargallo-Peiró, A., Revilla, G., Avila, M. & Houzeaux, G. (2022). A Level Set-Based Actuator Disc Model for Turbine Realignment in Wind Farm Simulation: Meshing, Convergence and Applications.
DOI: https://www.mdpi.com/1996-1073/15/23/8877 | UPC repository (Open Access)

Q3/2022

M. Aach, R. Sedona, A. Lintermann, G. Cavallaro, H. Neukirchen and M. Riedel, "Accelerating Hyperparameter Tuning of a Deep Learning Model for Remote Sensing Image Classification," in IEEE International Geoscience and Remote Sensing Symposium (IGARSS),  pp. 263-266, 2022

DOI: 10.1109/IGARSS46834.2022.9883257 | JuSER (Open Access)

Q3/2022

R. Sedona, C. Paris, L. Tian, M. Riedel and G. Cavallaro, "An Automatic Approach for the Production of a Time Series of Consistent Land-Cover Maps Based on Long-Short Term Memory," in IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 203-206, 2022

DOI: 10.1109/IGARSS46834.2022.9883257 | JuSER (Open Access)

Q3/2022

Hassanian, R., Riedel, M. & Bouhlali, L. (2022). The Capability of Recurrent Neural Networks to Predict Turbulence Flow via Spatiotemporal Features. IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems (ICCC 2022)

DOI: https://ieeexplore.ieee.org/document/9922754 | UoI repository (Open Access)

Q3/2022

Mira, D., Pérez-Sánchez, E. J., Borell, R. & Houzeaux, G. (2022). HPC-enabling technologies for high-fidelity combustion simulations.

DOI: 10.1016/j.proci.2022.07.222 | UPC (OpenAccess)

Q3/2022

Slaidiņš, I., Timrote, I., Zagorskis, V. & Cikovskis, L. (2022). Educational Service Platform for Artificial Intelligence Resources.

DOI: 10.1109/EAEEIE54893.2022.9820214 | ortus.rtu.lv (OpenAccess)

Q3/2022

Sumner, E. M.; Unnthorsson, R. & Riedel, M. (2022). Replicating Human Sound Localization with a Multi-Layer Perceptron.

DOI: 10.5281/zenodo.6797854

Q2/2022

Hassanian R., Riedel M., Helgadottir A.,Costa P. & Bouhlali L. (2022). JUWELS Booster -- Lagrangian Particle Tracking Data of a Straining Turbulent Flow Assessed Using Machine Learning and Parallel Computing. ParCFD 2022 conference, Alba, Italy

UoI repository (Open Access)

Q2/2022

Kesselheim, S., Herten, A., Krajsek, K., Ebert, J., Jitsev, J., Cherti, M., Langguth, M., Gong, B., Stadtler, S., Mozaffari, A., Cavallaro, G., Sedona, R., Schug, A., Strube, A., Kamath, R., Schultz, M. G., Riedel, M. & Lippert, T. (2022). JUWELS Booster -- A Supercomputer for Large-Scale AI Research.

DOI: 10.48550/arXiv.2108.11976

Q2/2022

Hassanian, R., Yeganeh, N. & Riedel, M. (2022). Numerical Investigation on the Acceleration Vibration Response of Linear Actuator.

DOI: 10.4236/oalib.1108625 | opinvisindi (OpenAccess)

Q2/2022

Hassanian, R. & Riedel, M. (2022). Mechanical Elements Analysis of Stewart Platform: Computational Approach.

DOI: 10.21275/SR2242009233

Q1/2022

Sumner, E. M., Aach, M., Lintermann, A., Unnthorsson, R. & Riedel, M. (2022). Speed-Up of Machine Learning for Sound Localization via High-Performance Computing. 2022 26th International Conference on Information Technology (IT)

DOI: 10.1109/IT54280.2022.9743519 | opinvisindi (OpenAccess)

Q1/2022

Hassanian, R., Riedel, M., & Yeganeh N. (2022). A Review in Context to Wind Effect on NOCT
Model for Photovoltaic Panel. Crimson Publisher

DOI:

Q1/2022

Hassanian, R., Riedel, M., Helgadottir A., Yeganeh N. & Unnthorsson R. (2022). Implicit Equation for Photovoltaic Module Temperature and Efficiency via Heat Transfer Computational Model. Thermo 2022

DOI:10.3390/thermo2010004

Q4/2021

Hassanian, R., Yeganeh, N., Unnthorsson, R. & Riedel, M. (2021). A Practical Approach for Estimating the Optimum Tilt Angle of a Photovoltaic Panel for a Long Period—Experimental Recorded Data. MDPI Journals 2021

DOI:10.3390/solar1010005

Q4/2021

Rüttgers, M., Waldmann, M., Schröder, W. & Lintermann, A. (2021). Machine-Learning-Based Control of Perturbed and Heated Channel Flows. High Performance Computing, Proceedings of the 36th International Conference, ISC High Performance 2021

DOI:10.1007/978-3-030-90539-2_1

Q3/2021

Benediktsson, J. A., Cavallaro, G. & Riedel, M. (2021). Practice and Experience in Using Parallel and Scalable Machine Learning in Remote Sensing from HPC Over Cloud to Quantum Computing. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

DOI:10.1109/IGARSS47720.2021.9554656 | JuSER (Open Access)

Q3/2021

Delilbasic, A., Cavallaro, G., Willsch, M., Melgani, F., Michielsen, K. & Riedel, M. (2021). Quantum Support Vector Machine Algorithms for Remote Sensing Data Classification. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

DOI:10.1109/IGARSS47720.2021.9554802 | JuSER (Open Access)

Q3/2021

Sedona, R., Paris, C., Cavallaro, G., Bruzzone, L. & Riedel, M. (2021). A High-Performance Multispectral Adaptation GAN for Harmonizing Dense Time Series of Landsat-8 and Sentinel-2 Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI:10.1109/JSTARS.2021.3115604

Q2/2021

Sedona, R., Barakat, C., Einarsson, P., Hassanian, Cavallaro, G., R., Book, M., Neukirchen, H., Lintermann, A. & Riedel, M. (2021). Practice and Experience in using Parallel and Scalable Machine Learning with Heterogenous Modular Supercomputing Architectures. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

DOI:10.1109/IPDPSW52791.2021.00019 | JuSER (Open Access)

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