Using netCDF (.nc) Data

PINNICLE supports reading observational or model data stored in the netCDF (.nc) format, a widely used standard in Earth science and machine learning for managing large, hierarchical datasets.

Overview

Similar in Using HDF5 (.h5) Data , the netCDF files store data are also in a hierarchical format using datasets and groups. PINNICLE can navigate these structures and extract relevant fields for use in physics-informed neural networks. It supports both structured gridded data and scattered point data stored in .nc containers.

Configuration

To use .nc data in PINNICLE, add a new data source with "source": "nc" in your configuration, and you need to set the mapping for both the coordinates and variables:

hp["data"] = {
   "MynetCDF": {
      "data_path": ncpath,
      "X_map": {"x":"surf_x", "y":"surf_y" },
      "name_map": {"s":"surf_elv", "u":"surf_vx", "v":"surf_vy", "a":"surf_SMB", "b":"bed_BedMachine"},
      "data_size": {"u": 5000, "v": 5000, "H": 5000, "s": 5000},
      "scaling": {"u":1.0/31536000, "v":1.0/31536000},
      "source": "nc"
    }
}

• "data_path": Path to the .nc file

• "X_map": set the name mapping of the coordinates in PINNICLE to the .nc file

• "name_map": set the name mapping of the variables in PINNICLE to the .nc file

• "data_size": Number of data points to randomly sample for each variable

• "scaling": scaling factors multiplied to the data, the key is the same as the "name_map"

• Set a variable to "None" to infer it is only used as a Dirichlet boundary condition

• If the key is not mentioned in "data_size", then the corresponding field will not use data from this file

See the Examples section for workflows that incorporate .nc datasets.