-
document
-
Ghavamian, F. (author)
We study the acceleration of the finite element method (FEM) simulations using machine learning (ML) models. Specifically, we replace computationally expensive (parts of) FEM models with efficient ML surrogates. We develop three methods to speed up FEM simulations. The primary difference between these models is their degree of intrusion into the...
doctoral thesis 2021
Source URL (retrieved on 2024-06-07 05:19): https://repository.tudelft.nl/islandora/search/%20?%3Bamp%3Bf%5B0%5D=mods_genre_s%3A%22report%22&%3Bamp%3Bf%5B1%5D=mods_subject_topic_ss%3A%22Westerschelde%22&%3Bamp%3Bf%5B2%5D=mods_subject_topic_ss%3A%22morphodynamic%5C%20models%22&%3Bamp%3Bsort=mods_originInfo_dateSort_dt%20asc&%3Bf%5B0%5D=mods_name_personal_author_namePart_family_ss%3A%22Pronk%22&collection=research&display=tud_default&f%5B0%5D=mods_genre_s%3A%22doctoral%5C%20thesis%22&f%5B1%5D=mods_subject_topic_ss%3A%22machine%5C%20learning%22&f%5B2%5D=mods_subject_topic_ss%3A%22finite%5C%20element%5C%20analysis%22