The fast, time-resolved prediction of turbulent flows, particularly in complex natural environments such as urban canopies or wind farms, remains a major challenge. Traditional high-fidelity approaches, including DNS and LES, are often prohibitively expensive and rely on uncertain model inputs. Recent advances in generative AI provide a promising new avenue for capturing flow dynamics efficiently while quantifying predictive uncertainties. We investigate how state-of-the-art generative models can be adapted for scientific modeling to enable reliable and scalable turbulence prediction, as explored in the projects below.
Turbulence Generation and Data Assimilation with Reduced-Dimensionality Models
Physics-Guided Generative Modeling for Wind Turbine Wake Prediction
Uncertainty Quantification in Urban Planning
Projekte
We propose replacing conventional sequential data assimilation pipelines—typically consisting of numerical solvers followed by a correction stage—with a scalable, data-driven alternative. Our framework integrates dimensionality-reduction techniques with a transformer-based diffusion model operating in a compact latent space, enabling efficient prediction and data assimilation of four-dimensional turbulent flow fields.
Current simulation methods of wind farms wakes lack physical realism and are limited in the integration of multi-source data. Here, we explore and develop a generative modeling framework for wind turbine wake prediction that is able to integrate physical constraints and multi-source measurements e.g., UAV and LiDAR.
Urban planning requires reliable assessments of how land-use changes affect local climate, yet high-resolution simulations are too costly for large ensembles and comprehensive uncertainty quantification. We propose a conditional diffusion model with a given land-use configuration as the conditioning input.
Selected Publications
- Steinbrenner, F., Turan, B., Teng, H., and Xiao, H. Turbulence generation and data assimilation in wall-bounded flows with a latent diffusion model. Submitted to Journal of Fluid Mechanics. Also available at https://arxiv.org/abs/2603.02143
- Wu, J.-L., Kashinath, K., Alberta, A., Chirila, D., Prabhat, M., and Xiao, H. Enforcing statistical constraints in generative adversarial networks for modeling chaotic dynamical systems. Journal of Computational Physics, 406, 109209, 2020. DOI: https://doi.org/10.1016/j.jcp.2019.109209
Funding and Acknowledgement
These Projects are funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2075 - 390740016. We acknowledge the support by the Stuttgart Center for Simulation Science (SC SimTech) and Carl-Zeiss-Stiftung (CZS, Carl Zeiss Foundation), project number P2021_0401. Funding and support is highly acknowledged.
Contacts
Fabian Steinbrenner
M.Sc.Research Associate
Baris Turan
M.Sc.Research Associate