Experiments for the paper "An Active Learning Reliability Method for Systems with Partially Defined Performance Functions"

This repository presents the AK-MCS algorithm with a Hierarchical Gaussian Processes on some benchmark problems alongside baseline methods.

Getting started

Simply install the requirements and run the script to reproduce our published results:

pip install requirements.txt

python run_all.py

Results will be output in directory tmp with 5 repeats by default. The experiments will by default run in parallel with n_jobs=15 for the repeats in order to save compute time. If your computer has fewer gpus then you can set the command line options: run_all.py [-h] [--n-repeats N_REPEATS] [--save-dir SAVE_DIR] [--n-jobs N_JOBS].

We used python3.9 but the script should also work in other python3.8.

If you use the package in your research please consider citing our paper:

Sadeghi, J., Mueller, R., & Redford, J. (2022). An Active Learning Reliability Method for Systems with Partially Defined Performance Functions. NeurIPS 2022 Workshop on Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems (GPSMDMS). doi:10.48550/ARXIV.2210.02168