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.
  • 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.
  • Taouil, N., Lim, H. D., Zang, B., & Azarpeyvand, M. (2024). Simulation of Atmospheric Boundary Layer in the Wind Tunnel Facility at the University of Bristol. In 14th UK Conference on Wind Engineering.
  • Taouil, N., Lim, H. D., Zang, B., & Azarpeyvand, M. (2024). Characterisation of the Boundary Layer Wind Tunnel Facility at the University of Bristol. In PHYSMOD Lyon 2024.

Journal Articles

  • Barragán, G., Hetherington, A., Sengupta, A., Abadía-Heredia, R., Garicano-Mena, J., & Le Clainche, S. L. (2025). HybriNet-Hybrid Neural Network-based framework for Multi-Parametric Database Generation, Enhancement, and Forecasting. arXiv preprint arXiv:2510.25625. https://doi.org/10.48550/arXiv.2510.25625
  • Barragán, G., Hetherington, A., Abadía-Heredia, R., Garicano-Mena, J., & Le 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., Pérez, J. M., & Le Clainche, S. (2025). Hybrid machine learning models based on physical patterns to accelerate CFD simulations: a short guide on autoregressive models. arXiv preprint arXiv:2504.06774. https://doi.org/10.48550/arXiv.2504.06774
  • 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). https://doi.org/10.1063/5.0284085
  • Eiximeno, B., Sanchis-Agudo, M., Miró, A., Rodríguez, I., Vinuesa, R., & Lehmkuhl, O. (2025). On Deep-Learning-Based Closures for Algebraic Surrogate Models of Turbulent Flows. Journal of Fluid Mechanics. https://doi.org/10.1017/jfm.2025.10610
  • Sanchis-Agudo, M., Cremades, A., Martinez-Sanchez, A., Lozano-Duran, A., & Vinuesa, R. (2025). X-CAL: Explaining Latent Causality in Physical Space for Fluid Mechanics. arXiv preprint arXiv:2601.03311. https://doi.org/10.48550/arXiv.2601.03311
  • Sanchis-Agudo, M., & Vinuesa, R. (2025). Pressure as boundary curvature: A variational approach to potential flows. Physics of Fluids, 37(8). https://doi.org/10.1063/5.0286411
  • Sanchis-Agudo, M., & Vinuesa, R. (2025). A Geometric Foundation for the Universal Laws of Turbulence. arXiv preprint arXiv:2512.00068. https://doi.org/10.48550/arXiv.2512.00068
  • Gutha, S. B. C., Vinuesa, R., & Azizpour, H. (2025). Mode-Seeking for Inverse Problems with Diffusion Models. arXiv preprint arXiv:2512.10524. https://doi.org/10.48550/arXiv.2512.10524
  • Vishwasrao, A., Gutha, S. B. C., Cremades, A., Wijk, K., Patil, A., Gorle, C., McKeon, B. J., Azizpour, H., & Vinuesa, R. (2025). Diff-SPORT: Diffusion-based Sensor Placement Optimization and Reconstruction of Turbulent flows in urban environments. arXiv preprint arXiv:2506.00214. https://doi.org/10.48550/arXiv.2506.00214
  • 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., & Le 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. https://doi.org/10.48550/arXiv.2507.00539
  • 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). https://doi.org/10.1063/5.0281638
  • Li, H., Amaduzzi, R., Mosca, G., Raghunathan Srikumar, S. K., Gambale, A., & Parente, A. (2026). Quantifying parametric uncertainty in turbulence model for the numerical simulation of atmospheric boundary layer flows using dimensionality reduction and Bayesian regression. Journal of Wind Engineering and Industrial Aerodynamics, 272, 106397. https://doi.org/10.1016/j.jweia.2026.106397
  • 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. https://doi.org/10.1016/j.buildenv.2025.113251
  • Bombardi, E., Nóvoa, A., Magri, L., & Parente, A. (2026). Robust Turbulence Model Optimisation via Parameter-Regularised Ensemble Kalman Filter for Urban Flow Applications. Submitted to Journal of Fluid Mechanics, February 2026.
  • Bombardi, E., Gambale, A., & Parente, A. (2025). Parameter-Regularised EnKF Framework for Turbulent Schmidt Number Optimisation in Urban Atmospheric Dispersion Modelling. Submitted to Journal of Wind Engineering and Industrial Aerodynamics, December 2025.
  • Giral, F., Manzano, Á., Gómez, I., Vinuesa, R., & Le Clainche, S. (2025). GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance. arXiv preprint arXiv:2601.11440. https://doi.org/10.48550/arXiv.2601.11440
  • Giral, F., Manzano, Á., Gómez, I., Koumoutsakos, P., & Le Clainche, S. (2025). From Geometry to Airflow: Generative Urban Flow Modeling with Graph Diffusion. arXiv preprint arXiv:2512.14725. https://doi.org/10.48550/arXiv.2512.14725