E-Books →Numerical Computation, Data Analysis and Software in Mathematics and Engineering

In recent years, mathematical models, numerical methods and data analysis have been paid more attention. After the finite-element method, the meshless method is another effective tool for solving science and enginnering problems. Numerical methods, such as the finite-element method, boundary element method and meshless method, have played important roles in numerical simulations of complicated problems in the science, engineering and society fields. Various numerical methods are presented to solve problems in different fields, and their corresponding computational efficiency, accuracy and convergence are also studied. With the development of big data, numerical simulation based on data analysis or big data will be an important direction for science and engineering computation. Deep learning is another new and effective approach to analyzing the properties of new materials.
The present book contains 14 articles that were accepted for publication in the Special Issue "Numerical Computation, Data Analysis and Software in Mathematics and Engineering" of the MDPI journal Mathematics. These articles were published in Volumes 9 (2021) and 10 (2022) of the journal. The topics of these articles include the aspects of meshless method, numerical simulation, mathematical model, deep learning and data analysis. Meshless methods, such as the improved element-free Galerkin method, the dimension-splitting, interpolating, moving, least squares method, the dimension-splitting, generalized, interpolating, element-free Galerkin method and the improved interpolating, complex-variable, element-free Galerkin method, are presented for some problems. Some complicated problems, such as the cold roll-forming process, ceramsite compound insulation block, crack propagation and heavy-haul railway tunnel with defects are numerically analyzed. Mathematical models, such as the lattice hydrodynamic model, extended car-following model and smart helmet-based PLS-BPNN Error compensation model, have been proposed. The use of the deep learning approach to predict the mechanical properties of single-network hydrogel is presented, and land-leasing data are analyzed and discussed. As the Guest Editor of this Special Issue, I am grateful to the authors of these articles for their high-quality contributions, to the reviewers for their valuable comments and to the administrative staff of MDPI publications for their support in completing this Special Issue. Special thanks to the Section Managing Editor Ms. Linn Li for her collaboration and valuable assisstance.
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