A stabilized node-based smoothed radial point interpolation method for functionally graded magneto-electro-elastic structures in thermal environment

Analysis of transient wave propagation dynamics using the enriched finite element method with interpolation cover functions

A stable node-based smoothed PFEM for solving geotechnical large deformation 2D problems

This paper presents a high-order node-based smoothed radial point interpolation method (NS-RPIM) with linear strain fields in smoothing domains. The linear smoothed strains are constructed by complete order of polynomial functions and normalized with reference to the central point of the smoothing region. The new NS-RPIM is one order higher than those of the existing methods which use piecewise constant strains. This high-order method still uses linear displacements within each triangular background cell, but linear strains are created over smoothing domains using the pick-out theory. Because the smoothed strain and the compatible strain within a local region are equal in an integral sense, the unknown parameters in the reconstructed strain functions can be determined uniquely by using three linearly independent weight functions. The numerical verification and computational efficiency are investigated and compared with the standard node-based smoothing models. It is found that the linear strain NS-RPIM shows improvement on both the convergence and computational efficiency in terms of error norm in displacement and is temporally stable. Since no spurious non-zero energy mode appears, this approach can be employed directly for the vibration analysis of solids.

A node-based smoothed radial point interpolation method with linear strain fields for vibration analysis of solids

The development of material science in the manufacturing industry has resulted in a huge amount of material data, which are often from different sources and vary in data format and semantics. The integration and fusion of material data can offer a unified framework for material data representation, processing, storage and mining, which can further help to accomplish many tasks, including material data disambiguation, material feature extraction, material-manufacturing parameters setting, and material knowledge extraction. On the other side, the rapid advance of information technologies like artificial intelligence and big data, brings new opportunities for material data fusion. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on material data fusion. This review first analyzes the special properties of material data and discusses the motivations of multi-source material data fusion. Then, we particularly focus on the recent achievements of multi-source material data fusion. This review has a few unique features compared to previous studies. First, we present a systematic categorization and comparison framework for material data fusion according to the processing flow of material data. Second, we discuss the applications and impact of recent hot technologies in material data fusion, including artificial intelligence algorithms and big data technologies. Finally, we present some open problems and future research directions for multi-source material data fusion.

Information Fusion for Multi-Source Material Data: Progress and Challenges

A novel node-based smoothed finite element method with linear strain fields for static, free and forced vibration analyses of solids

An efficient method to compute different types of generalized inverses based on linear transformation

In this paper, we derive a novel expression for the computation of the outer inverse 

$A_{T,S}^{(2)}$
. Based on this expression, we present a new Gauss-Jordan elimination method for computing 

$A_{R(G),N(G)}^{(2)}$
. The analysis of computational complexity indicates that our algorithm is more efficient than the existing Gauss-Jordan elimination algorithms for 

$A_{R(G),N(G)}^{(2)}$
 in the literature for a large class of problems. Especially for the case when G is a Hermitian matrix, our algorithm has the lowest computational complexity among the existing algorithms. Finally, numerical experiments show that our method for the outer inverse 

$A_{R(G),N(G)}^{(2)}$
 generally is more efficient than that of the other existing methods in the cases of matrices A with m < n or square matrices G with high rank or Hermitian matrices G in practice.

Yongshu Li

Papers

A novel node-based smoothed finite element method with linear strain fields for static, free and forced vibration analyses of solids

An efficient method to compute different types of generalized inverses based on linear transformation

An efficient method for computing the outer inverse AT,S(2)$A_{T,S}^{(2)}$ through Gauss-Jordan elimination