VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification
The GitHub repository contains sample codes for the case studies carried out in the research paper titled 'VB-DeepONet: A Bayesian operator learning framework for uncertainty quantification'. Please go through the research paper to understand the implemented algorithm. Note: Results may vary slightly for different iterations of programs as random initializations of neural network is involved.
Dataset Link: https://csciitd-my.sharepoint.com/:f:/g/personal/amz218308_iitd_ac_in/Ep2kkIW9rXFMs5UAvDFUWdwBC-iL1QwWmKxlVmfDJtEI1g?e=lg0dxa
** If there is some ambiguity in the datasets/codes please comment in the repository.
arXiv Citation details:
@article{garg2022variational, title={Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations}, author={Garg, Shailesh and Chakraborty, Souvik}, journal={arXiv preprint arXiv:2206.05655}, year={2022} }
**Citation details for the journal paper will be updated later