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pro vyhledávání: '"Fidkowski, Krzysztof"'
The use of deep learning has become increasingly popular in reduced-order models (ROMs) to obtain low-dimensional representations of full-order models. Convolutional autoencoders (CAEs) are often used to this end as they are adept at handling data th
Externí odkaz:
http://arxiv.org/abs/2402.05372
The use of reduced-order models (ROMs) in physics-based modeling and simulation almost always involves the use of linear reduced basis (RB) methods such as the proper orthogonal decomposition (POD). For some nonlinear problems, linear RB methods perf
Externí odkaz:
http://arxiv.org/abs/2204.08280
This work presents and compares efficient implementations of high-order discontinuous Galerkin methods: a modal matrix-free discontinuous Galerkin (DG) method, a hybridizable discontinuous Galerkin (HDG) method, and a primal formulation of HDG, appli
Externí odkaz:
http://arxiv.org/abs/1812.04789
Autor:
Fidkowski, Krzysztof J.
Publikováno v:
In Aerospace Science and Technology October 2022 129
Autor:
Fidkowski, Krzysztof J.
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 September 2022 399
Akademický článek
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Autor:
Chen, Guodong, Fidkowski, Krzysztof J.
Publikováno v:
In Computers and Fluids 15 June 2021 223
Autor:
Fidkowski, Krzysztof J., Chen, Guodong
Publikováno v:
In Journal of Computational Physics 1 February 2021 426
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.
Includes bibliographical references (p. 169-175).
While an indispensable tool in analysis and design applications, Computational Fluid Dyna
Includes bibliographical references (p. 169-175).
While an indispensable tool in analysis and design applications, Computational Fluid Dyna
Publikováno v:
In Aerospace Science and Technology December 2020 107