Scientific Outputs and Publications

Conference Papers

  • Koliogeorgi, K., Anagnostopoulos, G., Zampino, G., Sanchis, M., Vinuesa, R., & Xydis, S. (2024, March). Auto-tuning Multi-GPU High-Fidelity Numerical Simulations for Urban Air Mobility. In 2024 Design, Automation & Test in Europe Conference & Exhibition. (pp. 1-6). IEEE.
  • Vishwasrao, A., Gutha, S., Patil, A., Wijk, K., McKeon, B. J., Gorle, C., … & Vinuesa, R. Diffusion models for optimal sensor placement and sparse reconstruction for simplified urban flows.
  • Gutha, S. B. C., Vinuesa, R., & Azizpour, H. (2025, February). Inverse Problems with Diffusion Models: A MAP Estimation Perspective. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 4153-4162). IEEE.

Journal Articles

  • Barragán, G., Hetherington, A., Sengupta, A., Abadía-Heredia, R., Garicano-Mena, J., & Clainche, S. L. (2025). HybriNet-Hybrid Neural Network-based framework for Multi-Parametric Database Generation, Enhancement, and Forecasting. arXiv preprint arXiv:2510.25625.
  • Barragán, G., Hetherington, A., Abadía-Heredia, R., Garicano-Mena, J., & Clainche, S. L. (2025). HOSVD-SR: A Physics-Based Deep Learning Framework for Super-Resolution in Fluid Dynamics. arXiv preprint arXiv:2504.17994.
  • Sengupta, A., Abadía-Heredia, R., Hetherington, A., Perez Perez, J. M., & Le Clainche, S. (2025). Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models. Available at SSRN 5236454.
  • Sanchis-Agudo, M., Wang, Y., Arnau, R., Guastoni, L., Lim, J., Duraisamy, K., & Vinuesa, R. (2025). Easy attention: A simple attention mechanism for temporal predictions with transformers. APL Computational Physics, 1(1).
  • Eiximeno, B., Sanchís-Agudo, M., Miró, A., Rodríguez, I., Vinuesa, R., & Lehmkuhl, O. (2024). On Deep-Learning-Based Closures for Algebraic Surrogate Models of Turbulent Flows. arXiv preprint arXiv:2412.04239.
  • Sanchis-Agudo, M., & Vinuesa, R. (2025). Pressure as boundary curvature: A variational approach to potential flows. Physics of Fluids, 37(8).
  • Vishwasrao, A., Gutha, S. B. C., Cremades, A., Wijk, K., Patil, A., Gorle, C., … & Vinuesa, R. (2025). Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments. arXiv preprint arXiv:2506.00214.
  • Gutha, S. B. C., Vinuesa, R., & Azizpour, H. (2025, February). Inverse Problems with Diffusion Models: A MAP Estimation Perspective. In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (pp. 4153-4162). IEEE.
  • Vishwasrao, A., Gutha, S., Patil, A., Wijk, K., McKeon, B. J., Gorle, C., … & Vinuesa, R. Diffusion models for optimal sensor placement and sparse reconstruction for simplified urban flows.
  • Jeanney, P., Hetherington, A., Ahmed, S. E., Lanceta, D., Saiz, S., Perez, J. M., & CLainche, S. L. (2025). Ensemble Kalman Filter for Data Assimilation coupled with low-resolution computations techniques applied in Fluid Dynamics. arXiv preprint arXiv:2507.00539.
  • Bombardi, E., Gambale, A., & Parente, A. (2025). A review of ABL modelling in RANS simulations: Inlet conditions and turbulence models. Building and Environment, 283, 113251.
  • Li, H., Procacci, A., Raghunathan Srikumar, S. K., Mosca, G., Gambale, A., & Parente, A. (2025). A clustering-based domain decomposition framework for reduced-order modeling: Application to atmospheric boundary layer flows. Physics of Fluids, 37(9).
  • Li, H., Procacci, A., Srikumar, S. K. R., Mosca, G., Gambale, A., & Parente, A. Development of Multi-Region Reduced-Order Modeling Framework: Application in Atmospheric Boundary Layer Flows. Available at SSRN 5202104.