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227 results on '"Tang, Shaoqiang"'

1. Efficient Matching Boundary Conditions of Two-dimensional Honeycomb Lattice for Atomic Simulations

Author

Liu, Baiyili, Ji, Songsong, Pang, Gang, Tang, Shaoqiang, and Zhang, Lei

Subjects
Mathematics - Numerical Analysis, Condensed Matter - Materials Science
Abstract

In this paper, we design a series of matching boundary conditions for a two-dimensional compound honeycomb lattice, which has an explicit and simple form, high computing efficiency and good effectiveness of suppressing boundary reflections. First, we formulate the dynamic equations and calculate the dispersion relation for the harmonic honeycomb lattice, then symmetrically choose specific atoms near the boundary to design different forms of matching boundary conditions. The boundary coefficients are determined by matching a residual function at some selected wavenumbers. Several atomic simulations are performed to test the effectiveness of matching boundary conditions in the example of a harmonic honeycomb lattice and a nonlinear honeycomb lattice with the FPU-$\beta$ potential. Numerical results illustrate that low-order matching boundary conditions mainly treat long waves, while the high-order matching boundary conditions can efficiently suppress short waves and long waves simultaneously. Decaying kinetic energy curves indicate the stability of matching boundary conditions in numerical simulations.

Published
2024

6. HiDeNN-PGD: reduced-order hierarchical deep learning neural networks

Author

Zhang, Lei, Lu, Ye, Tang, Shaoqiang, and Liu, Wing Kam

Subjects
Mathematics - Numerical Analysis, Computer Science - Machine Learning
Abstract

This paper presents a proper generalized decomposition (PGD) based reduced-order model of hierarchical deep-learning neural networks (HiDeNN). The proposed HiDeNN-PGD method keeps both advantages of HiDeNN and PGD methods. The automatic mesh adaptivity makes the HiDeNN-PGD more accurate than the finite element method (FEM) and conventional PGD, using a fraction of the FEM degrees of freedom. The accuracy and convergence of the method have been studied theoretically and numerically, with a comparison to different methods, including FEM, PGD, HiDeNN and Deep Neural Networks. In addition, we theoretically showed that the PGD converges to FEM at increasing modes, and the PGD error is a direct sum of the FEM error and the mode reduction error. The proposed HiDeNN-PGD performs high accuracy with orders of magnitude fewer degrees of freedom, which shows a high potential to achieve fast computations with a high level of accuracy for large-size engineering problems., Comment: 35 pages, 12 figures

Published
2021
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