Zobrazeno 1 - 10
of 97
pro vyhledávání: '"Kim, Hyea Hyun"'
Partitioned neural network functions are used to approximate the solution of partial differential equations. The problem domain is partitioned into non-overlapping subdomains and the partitioned neural network functions are defined on the given non-o
Externí odkaz:
http://arxiv.org/abs/2312.14370
In this paper, neural network approximation methods are developed for elliptic partial differential equations with multi-frequency solutions. Neural network work approximation methods have advantages over classical approaches in that they can be appl
Externí odkaz:
http://arxiv.org/abs/2311.03746
Autor:
Yang, Hee Jun, Kim, Hyea Hyun
To enhance solution accuracy and training efficiency in neural network approximation to partial differential equations, partitioned neural networks can be used as a solution surrogate instead of a single large and deep neural network defined on the w
Externí odkaz:
http://arxiv.org/abs/2211.00225
Autor:
Yang, Hee Jun, Kim, Hyea Hyun
Publikováno v:
In Computers and Mathematics with Applications 15 September 2024 170:237-259
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 September 2024 429
Publikováno v:
In Computers and Mathematics with Applications 15 May 2024 162:180-195
Autor:
Park, Eun-Hee, Kim, Hyea Hyun
Publikováno v:
In Journal of Computational and Applied Mathematics April 2024 440
In this paper, we consider the balancing domain decomposition by constraints (BDDC) algorithm with adaptive coarse spaces for a class of stochastic elliptic problems. The key ingredient in the construction of the coarse space is the solutions of loca
Externí odkaz:
http://arxiv.org/abs/2104.09162
A two-level overlapping Schwarz method is developed for second order elliptic problems with highly oscillatory and high contrast coefficients, for which it is known that the standard coarse problem fails to give a robust preconditioner. In this paper
Externí odkaz:
http://arxiv.org/abs/1901.00112
Publikováno v:
In Journal of Computational Physics 1 October 2022 466