Deep Gaussian Process Emulation using Stochastic Imputation

The package dgpsi implements inference of deep Gaussian process emulation using stochastic imputation.

Key features

dgpsi currently has the following features:

• Flexible deep Gaussian process architecture construction:
  • multiple layers;
  • multiple GP nodes;
  • separable or non-separable squared exponential and Matérn2.5 kernels;
  • global input connections;
• Emulation of feed-forward systems of computer models:
  • linking GP emulators of individual computer models;
  • linking GP and DGP emulators of individual computer models;
• More features coming soon.

Please see demo for some illustrative examples of the method. Detailed descriptions on how to use the package can be found in scripts contained in dgpsi.

Installation

After cloning the repo, type the following in the same directory of setup.py:

pip install .

to install the code and its required dependencies.

Built with

The package is built under Python 3.7.3 with following packages:

• numpy 1.18.2;
• numba 0.51.2;
• matplotlib 3.2.1;
• tqdm 4.50.2;
• scikit-learn 0.22.0;
• scipy 1.4.1.

Contact

Please feel free to email me with any questions and feedbacks:

Deyu Ming <[email protected]>.

References

Ming, D., Williamson, D., and Guillas, S. (2021) Deep Gaussian process emulation using stochastic imputation.

Ming, D. and Guillas, S. (2021) Linked Gaussian process emulation for systems of computer models using Matérn kernels and adaptive design, SIAM/ASA Journal on Uncertainty Quantification. In press.