Update README.md

README.md

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 [![GitHub](https://img.shields.io/badge/GitHub-rbischof/windinet-blue?logo=github)](https://github.com/rbischof/windinet)
 [![Web Demo](https://img.shields.io/badge/Web%20Demo-Launch-orange)](https://rbischof.github.io/windinet_web/)
 
-**WinDiNet** repurposes a 2-billion-parameter video diffusion transformer ([LTX-Video](https://github.com/Lightricks/LTX-Video)) as a fast, differentiable surrogate for computational fluid dynamics (CFD) simulations of urban wind patterns. Fine-tuned on 10,000 CFD simulations across procedurally generated building layouts, it generates complete **112-frame wind field rollouts in under one second** — over 2,000x faster than traditional CFD solvers.
+**WinDiNet** repurposes a 2-billion-parameter video diffusion transformer ([LTX-Video](https://github.com/Lightricks/LTX-Video)) as a fast, differentiable surrogate for computational fluid dynamics (CFD) simulations of urban wind patterns. Fine-tuned on 10,000 CFD simulations across procedurally generated building layouts, it generates complete **112-frame wind field rollouts in under one second** — over 2,000x faster than the ground truth CFD solver.
 
 - **Physics-informed VAE decoder**: Fine-tuned with incompressibility and wall boundary losses for physically consistent velocity field reconstruction
 - **Scalar conditioning**: Fourier-feature-encoded inlet speed and domain size replace text prompts, enabling precise physical parametrisation