mfmodeling
Multi-fidelity modeling using Gaussian process regression
Purpose of this project
This project is motivated to apply multi-fidelity data fusion algorithms to the regression problem in turbulent transport modeling in magnetic fusion plasma. The developed module will be available as a general tool for multi-fidelity regression problems.
Usage
mfmodeling module requires external packages: numpy, GPyTorh.
The following is an explanation on the simple usage of NARGP (Nonlinear AutoRegressive Gaussian Process regression [P. Perdikaris (2017)]) as a multi-fidelity regression algorithm. See also examples/NARGP_example_2d/Demo_May2024_NARGP_example_2d.ipynb.
- Prepare multi-fidelity datasets as a list of each fidelity data.
data_list = [[data_lowfid_x, data_lowfid_y],
[data_highfid_x, data_highfid_y]]
- Instantiate the NARGP object using the above dataset.
from mfmodeling import NARGP
model_nargp = NARGP(data_list = data_list)
- Optimize hyperparameters of the kernel function in NARGP.
model_nargp.optimize()
- Make a prediction.
mean, var = model_nargp.prediction(x_pred)
where mean and var are the prediction of posterior mean and variance at your evaluating position x_pred.
Reference
1 maintainer
Metadata
1 maintainer
Release files for mfmodeling 1.0.3
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