Normalization files

This folder provides the normalization (standardization) parameters used for FuXi-CFD inference.

Overview

Inputs are standardized before being fed to the ONNX model.
Model outputs are de-standardized back to physical scale after inference.

Files

scaler_input.npy : dict of input normalization stats (mean/std) for static/dynamic input variables
scaler_output.npy : dict of output normalization stats (mean/std) for model outputs Both are stored as Python dicts inside .npy (load with allow_pickle=True).

Input normalization (`scaler_input.npy`)

Keys:

low_mean, low_std for low-res dynamic inputs: ["u_100m", "v_100m"]
high_mean, high_std for high-res static inputs: ["dem", "roughness"]

Final ONNX input channel order must be: [u_100m, v_100m, dem, roughness] (C=4)

Output de-normalization (`scaler_output.npy`)

Keys:

mean, std: arrays of shape (27, 4) (levels=27, vars=4)

Output variable order: [u, v, w, k]

The ONNX model output tensor is expected as: (1, 27, 4, 300, 300) and de-normalization applies: pred = pred * std + mean (broadcast on (300,300))

Example loading

import numpy as np
in_stats = np.load("normalization/scaler_input.npy", allow_pickle=True).item()
out_stats = np.load("normalization/scaler_output.npy", allow_pickle=True).item()