Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Published in Proceedings of the 41st International Conference on Machine Learning (ICML), 2024
Recommended citation: Mouli, S.C., Maddix, D.C., Alizadeh, S., Gupta, G., Wang, Y., Stuart, A., Mahoney, M.W. (2024). "Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs." Proceedings of the 41st International Conference on Machine Learning (ICML), PMLR. 235:36372-36418. https://proceedings.mlr.press/v235/mouli24a.html
Our Operator-ProbConserv code is on the amazon-science github.