Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Katiana Kontolati"'
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-11 (2024)
Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by app
Externí odkaz:
https://doaj.org/article/946341210a94489cb89fd883d3b4e2b9
Autor:
Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukas Novak, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner
Publikováno v:
SoftwareX, Vol 24, Iss , Pp 101561- (2023)
This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to sim
Externí odkaz:
https://doaj.org/article/a3229a45b73149a4a3580b2909b45778
Autor:
Katiana Kontolati, Panagiotis Tsilifis, Sayan Ghosh, Valeria Andreoli, Michael Shields, Liping Wang
Publikováno v:
AIAA SCITECH 2023 Forum.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::76c5f8362c39aad2af687a10c92ac9b9
https://doi.org/10.2514/6.2023-0918
https://doi.org/10.2514/6.2023-0918
Transfer learning (TL) enables the transfer of knowledge gained in learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labeling, potential computational power limitation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c33ce8cadc631840e3fe9692c2b2f87b
http://arxiv.org/abs/2204.09810
http://arxiv.org/abs/2204.09810
Autor:
Chris H. Rycroft, Katiana Kontolati, Darius Alix-Williams, Michael L. Falk, Michael D. Shields, Nicholas M. Boffi
We introduce a generalized machine learning framework to probabilistically parameterize upper-scale models in the form of nonlinear PDEs consistent with a continuum theory, based on coarse-grained atomistic simulation data of mechanical deformation a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::58aff726540b087088c6bc5f21b06b83
https://escholarship.org/uc/item/2hx5g706
https://escholarship.org/uc/item/2hx5g706
We perform one and two-parameter numerical bifurcation analysis of a mechanotransduction model approximating the dynamics of mesenchymal stem cell differentiation into neurons, adipocytes, myocytes and osteoblasts. For our analysis, we use as bifurca
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::03b05f3e6c8cae05c6f2ba9f10055ff5
http://hdl.handle.net/11588/752567
http://hdl.handle.net/11588/752567