1 code implementation • 4 Apr 2024 • Tyler Chang, Andrew Gillette, Romit Maulik
In this work, we present a best-of-both-worlds approach to verifiable scientific machine learning by demonstrating that (1) multiple standard interpolation techniques have informative error bounds that can be computed or estimated efficiently; (2) comparative performance among distinct interpolants can aid in validation goals; (3) deploying interpolation methods on latent spaces generated by deep learning techniques enables some interpretability for black-box models.
no code implementations • 13 Jul 2022 • Andrew Gillette, Brendan Keith, Socratis Petrides
In this work, we revisit the marking decisions made in the standard adaptive finite element method (AFEM).
