Description

BayesProcess is a python package for Physics informed Bayesian network inference using neural network surrogate model for matching process / variable / performance in solar cells.

Installation

To install, just clone the following repository:

pip install -r requirements.txt

https://github.com/PV-Lab/BayesProcess.git

Usage

run surrogate_model.py , with the given datasets to create the neural network surrogate for numerical PDE solver. run Bayes.py with the saved surrogate model. This performs Bayesian network inference to map the process variable (Temperature) to material descriptors. The package contains the following module and scripts:

Module	Description
JV_surrogate.py	Script for training neural network JV surrogate model
Bayes.py	Script for Bayesian inference using MCMC
requirements.txt	required packages

Authors

"Danny" Zekun Ren and Felipe Oviedo