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pro vyhledávání: '"Gladstone, Rini Jasmine"'
Autor:
Gladstone, Rini Jasmine, Rahmani, Helia, Suryakumar, Vishvas, Meidani, Hadi, D'Elia, Marta, Zareei, Ahmad
Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs. However, time-independent problems pose the challenge of requiring
Externí odkaz:
http://arxiv.org/abs/2303.15681
Robust topology optimization (RTO), as a class of topology optimization problems, identifies a design with the best average performance while reducing the response sensitivity to input uncertainties, e.g. load uncertainty. Solving RTO is computationa
Externí odkaz:
http://arxiv.org/abs/2107.10661
Physics-Informed Neural Networks (PINNs) are a class of deep neural networks that are trained, using automatic differentiation, to compute the response of systems governed by partial differential equations (PDEs). The training of PINNs is simulation-
Externí odkaz:
http://arxiv.org/abs/2104.12325
Autor:
Gladstone, Rini Jasmine1 (AUTHOR), Rahmani, Helia2 (AUTHOR), Suryakumar, Vishvas2 (AUTHOR), Meidani, Hadi1 (AUTHOR), D'Elia, Marta3 (AUTHOR) marta.delia@simulation.science, Zareei, Ahmad2 (AUTHOR)
Publikováno v:
Scientific Reports. 2/9/2024, Vol. 14 Issue 1, p1-14. 14p.
Topology Optimization is the process of finding the optimal arrangement of materials within a design domain by minimizing a cost function, subject to some performance constraints. Robust topology optimization (RTO) also incorporates the effect of inp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5518c1227b669fd73bdce683b3e3ab73