Setup
Pre
Outdoor
Outdoor+
Indoor
Sun Shadow Daylight
MRT
Post
Post Comfort
LBM
ML
11 ML
Main Components
ML Model Download an ONNX wind-prediction model from HuggingFace for the Wind Predictor component. Yel 2.0 is public; Esen 1.0 and Poyraz 1.0 need a HuggingFace token. All are 8-channel Wind Predictor models. (Yel 1.0 is a different architecture — the GAN image model the hosted Wind Predictor (Cloud) component runs via its API — and cannot be loaded here.) Models cache in ~/Eddy3D/Models/ and are reused on subsequent runs (a model already downloaded to the old ~/SUS_LAB/ folder is moved over, not re-fetched). Method: Kastner et al. (2026), SSRN preprint 6401886, doi:10.2139/ssrn.6401886.
Wind Predictor Run ONNX wind-field prediction end-to-end. Computes SDF, building height, Zrelative, U/Uref, direction features from geometry, assembles the 8-channel input tensor, runs ONNX inference, and outputs predicted wind speeds. Supports legacy 1ch (U), 2ch (U + k) and new 4ch (U + k + Uroof + kroof) models. Method: Kastner et al. (2026), SSRN preprint 6401886, doi:10.2139/ssrn.6401886.
Wind Predictor Cloud Predict a pedestrian wind-speed field from buildings without running CFD, using the hosted Eddy3D model (Yel 1.0, a 512x512 image GAN). Rasterizes the buildings and the analysis plane into the model's input image, sends it to the API, and returns the predicted wind speeds plus a colored result mesh. Runs on Eddy3D's server: needs internet, no GPU and no model download, and the free server may need a minute to wake up. For a local GPU run over arbitrary points and multiple wind directions, use Wind Predictor with a model from ML Model instead. Method: Kastner & Dogan (2023), Building and Environment 242:110384, doi:10.1016/j.buildenv.2023.110384.
Wind Comfort Predictor ML Calculate Pedestrian Wind Comfort using predicted wind fields from the ONNX model. Method: Kastner et al. (2026), SSRN preprint 6401886, doi:10.2139/ssrn.6401886.
Dataset Curator Compute and export wind dataset features from analysis points and building geometry.
Dataset Reader Read processed CSV datasets back into Grasshopper. Supports mag_U and all spatial features.
Interpolate UMag Resample direction-specific wind-magnitude fields onto a new point grid (nearest-neighbour average, with direction-specific rotation). Prepares grids for GAN applications.
MRT Dataset Curator Export the solved MRT field as a machine-learning dataset: one row per sensor per hour with spatial features, hourly climate drivers and the MRT/UTCI targets.