license: apache-2.0
tags:
- wind-simulation
- cfd
- video-diffusion
- urban-design
- physics-informed
- surrogate-model
WinDiNet: Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows
WinDiNet repurposes a 2-billion-parameter video diffusion transformer (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
- Differentiable end-to-end: Enables gradient-based inverse design of urban building layouts for pedestrian wind comfort
- State-of-the-art accuracy: Outperforms specialised neural operators (FNO, OFormer, Poseidon, U-Net) on vRMSE, spectral divergence, and Wasserstein distance
Model Weights
This repository contains three checkpoint files:
| File | Description | Parameters | Size |
|---|---|---|---|
dit.safetensors |
Fine-tuned diffusion transformer | 1.92B | 7.7 GB |
scalar_embedding.safetensors |
Fourier feature scalar conditioning module | 4.3M | 17 MB |
vae_decoder.safetensors |
Physics-informed VAE decoder | 553M | 2.2 GB |
Download
Checkpoints are downloaded automatically when using the windinet package. For manual download:
# Using Hugging Face CLI
huggingface-cli download rabischof/windinet --local-dir checkpoints/
# Or individual files
huggingface-cli download rabischof/windinet dit.safetensors
huggingface-cli download rabischof/windinet scalar_embedding.safetensors
huggingface-cli download rabischof/windinet vae_decoder.safetensors
Installation
git clone https://github.com/rbischof/windinet.git
cd windinet
pip install -e .
Inference
Each input sample is a building footprint PNG (black=building, white=fluid) paired with a JSON file specifying inlet conditions:
{"inlet_speed_mps": 10.0, "field_size_m": 1400}
Run inference:
python scripts/inference.py configs/inference.yaml \
--input_dir examples/footprints/ \
--out_dir predictions/
Outputs per sample: .npz (u/v velocity fields in m/s, float16) and .mp4 (wind magnitude video).
Training
WinDiNet training has two stages:
Stage 1: VAE decoder fine-tuning with physics-informed losses (incompressibility + wall boundary conditions):
python scripts/finetune_vae.py configs/finetune_vae.yaml
Stage 2: Diffusion transformer training with scalar conditioning:
python scripts/train.py configs/windinet_scalar.yaml
See the GitHub repository for dataset preparation and configuration details.
Inverse Design
WinDiNet serves as a differentiable surrogate for gradient-based optimisation of building layouts:
python scripts/inverse_design.py configs/inverse_opt.yaml
The optimizer adjusts building positions to minimise a Pedestrian Wind Comfort (PWC) loss. The framework is extensible to custom objectives and building parametrisations — see inverse/objective.py and inverse/footprint.py.
Citation
@article{perini2025windinet,
title={Pretrained Video Models as Differentiable Physics Simulators for Urban Wind Flows},
author={Perini, Janne and Bischof, Rafael and Arar, Moab and Duran, Ay{\c{c}}a and Kraus, Michael A. and Mishra, Siddhartha and Bickel, Bernd},
journal={arXiv preprint arXiv:2603.21210},
year={2026}
}
Details
- License: Apache 2.0
- Base model: LTX-Video 2B v0.9.6
- Training data: 10,000 CFD simulations (256x256, 112 frames each)
- arXiv: 2603.21210
- Authors: Janne Perini*, Rafael Bischof*, Moab Arar, Ayca Duran, Michael A. Kraus, Siddhartha Mishra, Bernd Bickel (* equal contribution)