11 ML

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).

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.

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.

Calculate Pedestrian Wind Comfort using predicted wind fields from the ONNX model.

Compute and export wind dataset features from analysis points and building geometry.

Read processed CSV datasets back into Grasshopper. Supports mag_U and all spatial features.

Resample direction-specific wind-magnitude fields onto a new point grid (nearest-neighbour average, with direction-specific rotation). Prepares grids for GAN applications.

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.