ShenQianli / PICProp

Official implementation of PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification (NeurIPS 2023)

PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification

This is the official implementation of PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification (NeurIPS 2023).

Arxiv: https://arxiv.org/abs/2310.06923

Please feel free to shoot an email to [e-mail] for any questions.

Usage:

Install required python packages:

pip install -r requirements.txt

Add the your/path/to/PICProp to $PYTHONPATH:

export PYTHONPATH=$PYTHONPATH:your/path/to/PICProp

Enter desired experiment directory, for example:

cd your/path/to/PICProp/experiment/pedagogical

Execute scripts to start training. For PICProp in a brute-force manner:

python run_picprop.py

For EffiPICProp,

python run_effipicprop.py --lamb $lamb

The script will automatically sweep required query points and start a PICProp run if the result is absent, and summarize the result in a basic plot.