Python Tool for Training RBF Surrogate Models

Evan Chodora - echodor@clemson.edu

python rbf_surrogate.py -h for help and options

Can be used with a variety of RBFs (see the dictionary of function names below) and can be used with both multi-dimensional inputs and multi-dimensional outputs (and scalars for both).

Makes use of the Spatial Distance calculation functions from SciPy to compute the radial distance matrices for the radial basis function calculations. (https://docs.scipy.org/doc/scipy/reference/spatial.distance.html)

Included RBFs:

• Linear: "linear"
• Cubic: "cubic"
• Absolute Value: "absolute"
• Multiquadratic: "multiquadratic"
• Inverse Multiquadratic: "inverse_multiquadric"
• Gaussian: "gaussian"
• Thin Plate: "thin_plate"

Prerequisites

• Python (version 3 and above)
• numpy
• scipy

Training Example:

python rbf_surrogate.py -t train -r gaussian -x x_data.dat -y y_data.dat -m model.db

In this example, the RBF surrogate program is called to train a new surrogate using the input parameter file x_data.dat and the output (response) file y_data.dat (see the file format specified below). This trained emulator is generated using a Gaussian Radial Basis Function and the model is stored in the file model.db. When the program runs, the RBF weights will be computed and the model paramter objects will be saved as a Python shelve (https://docs.python.org/3/library/shelve.html) database for later use.

Prediction Example

The code below creates an output file y_pred.dat based on the supplied previously trained surrogate model, model.db, at the query locations specified in x_pred.dat:

python rbf_surrogate.py -t predict -x x_pred.dat -m model.db

Input and Output File Format:

Files can be supplied in a comma-separated value format for x (input parameters) and y (output/response parameters) with a header line. Prediction files will be generated in the same format with the number of columns corresponding to the number of trained response dimensions and the number of rows equal to the number of query locations requested.

Example (x_data.dat):

x0,x1,x2
1.34,5,5.545
3.21,0.56,9.34