Julia Language in Computational Mechanics: A New Competitor

Xiao, Lei; Mei, Gang; Xi, Ning; Piccialli, Francesco

doi:10.1007/s11831-021-09636-0

Cited by 5 publications

(7 citation statements)

References 78 publications

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“…Instead of OOP, it relies on generic functions and a rich type system, making interoperability and modularity more straightforward. Although being young, it is getting a fast adoption among data science practitioners (bezanson2012julia ; Xiao et al, 2022).…”

Section: Textgraphsmentioning

confidence: 99%

NLP and Graph embeddings: TextGraphs.jl

Argôlo¹,

Gondim²,

Lopes-Rocha³

et al. 2023

Preprint

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Natural language processing (NLP) techniques analyze formal properties of speechin humans. One way of analyzing language is through graph embeddings. Graphs are cunningly simple abstractions, which have vertices and nodes as basic elements. Nevertheless, graphs were revealed to be very useful abstractions to describe associations in real-world phenomena whenever there are coupled elements. Graph embeddings associate words with nodes and adjacency in a phrase with links (edges). When words are repeated, the respective node is linked twice. Some attributes of these “speech graphs” are correlated with mental states, such as connectivity (e.g. largest connected components).The first section of this article reviews theoretical basis for the analysis of text-based graph embedding (”Text Graphs” or ”Speech Graphs”). We present standard procedures of text embedding and discuss graph properties (e.g. connected components) in this context.Some features brought by TextGraphs.jl (e.g. density, centrality) were not used before in literature. The second section covers applications, including installation, basic features, and vignettes with example cases

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Section: Textgraphsmentioning

confidence: 99%

NLP and Graph embeddings: TextGraphs.jl

Argôlo¹,

Gondim²,

Lopes-Rocha³

et al. 2023

Preprint

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“…Developers can select the appropriate parallelism method for their needs. Parallel computing on many-core GPUs can be conducted by using specific packages or utilizing the built-in function of Julia and parallel arrays [1].…”

Section: The Julia Languagementioning

confidence: 99%

“…Currently, numerical methods are the most important tools for solving various scientific and engineering problems [1]. For example, the Finite Element Method (FEM), one of the most successful numerical methods, has been widely employed in different scientific and engineering fields because of its mathematically rigorous proof and satisfactory efficiency [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…However, the shortcomings and deficiencies of FEM are becoming increasingly significant [2,[5][6][7][8]. (1) The FEM applies the problem domain of finite degrees of freedom to the problem domain of infinite degrees of freedom, which makes the system stiffness matrix "too rigid". (2) The conventional FEM has high requirements for mesh quality and cannot deal with distorted meshes.…”

Section: Introductionmentioning

confidence: 99%

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epSFEM: A Julia-Based Software Package of Parallel Incremental Smoothed Finite Element Method (S-FEM) for Elastic-Plastic Problems

et al. 2022

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In this paper, a parallel Smoothed Finite Element Method (S-FEM) package epSFEM using incremental theory to solve elastoplastic problems is developed by employing the Julia language on a multicore CPU. The S-FEM, a new numerical method combining the Finite Element Method (FEM) and strain smoothing technique, was proposed by Liu G.R. in recent years. The S-FEM model is softer than the FEM model for identical grid structures, has lower sensitivity to mesh distortion, and usually produces more accurate solutions and a higher convergence speed. Julia, as an efficient, user-friendly and open-source programming language, balances computational performance, programming difficulty and code readability. We validate the performance of the epSFEM with two sets of benchmark tests. The benchmark results indicate that (1) the calculation accuracy of epSFEM is higher than that of the FEM when employing the same mesh model; (2) the commercial FEM software requires 10,619 s to calculate an elastoplastic model consisting of approximately 2.45 million triangular elements, while in comparison, epSFEM requires only 5876.3 s for the same computational model; and (3) epSFEM executed in parallel on a 24-core CPU is approximately 10.6 times faster than the corresponding serial version.

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“…In this context, recent studies by Xiao et al (2021) indicate that Julia is challenging the 'natural' notions that high-level language programs must be slow or that machine performance requires sacrificing human convenience. Julia achieves this by incorporating computer science techniques such as multiparadigm programming, multiple dispatch, dynamic typing, and the use of package libraries in C and Python.…”

Section: Introductionmentioning

confidence: 99%

Julia Language Implementation of the Finite Element Method for Linear Instability of Plane Frames: An Efficient Alternative for Structural Analysis

Lobo,

Silva

2024

Lat. Am. j. solids struct.

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This paper introduces the Julia programming language as a dynamic, cost-effective, and efficient framework for implementing structural analysis packages. To achieve this, the finite element method was implemented for plane frames addressing the elastic instability problem through the Finite Element Method (FEM). Julia is a language open source, multiplatform, high-level and high-performance for technical and scientific computing, its compiler allows you to achieve speeds comparable to languages such as C and FORTRAN, but with more productive development dynamics due to its programming flexibility. Benchmarks between Julia and MATLAB are employed to discuss the processing costs, the programming techniques and paradigms used for computational performance. The results demonstrate that Julia performed the same analysis as the language used for comparison in 88.40% of the time, in addition to the fact that in loops comparisons case it reached 41.7% of the time for iteration, confirming its significant potential as a development tool of computational packages for structural analysis and scientific computing in general.

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Julia Language in Computational Mechanics: A New Competitor

Cited by 5 publications

References 78 publications

NLP and Graph embeddings: TextGraphs.jl

NLP and Graph embeddings: TextGraphs.jl

epSFEM: A Julia-Based Software Package of Parallel Incremental Smoothed Finite Element Method (S-FEM) for Elastic-Plastic Problems

Julia Language Implementation of the Finite Element Method for Linear Instability of Plane Frames: An Efficient Alternative for Structural Analysis

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