Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

Ly, Haï‐Bang; Vu, Huong-Lan Thi; Ho, Lanh Si; Pham, Binh Thai

doi:10.1007/s11709-022-0812-6

Cited by 2 publications

(1 citation statement)

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“…Ly et al reviewed methods for solving partial differential equations using physics-informed neural networks (PINNs), a novel residual-based adaptive optimization method proposed to improve the iterative efficiency of PINNs. This paper also provides a Python program library DeepXDE for the implementation of PINNs; the program library DeepXDE can solve the forward problem of given initial value conditions and boundary value conditions and can also solve the inverse problem given additional measurement results [12].…”

Section: Literature Reviewmentioning

confidence: 99%

Indifference Computer Experiment for Mathematical Identification of Two Variables

2022

Wireless Communications and Mobile Computing

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In order to understand the two types of nonlinear differential equation problems in engineering dynamics, the author proposes a numerical analysis method for the two types of nonlinear differential equations based on computer simulation. This method establishes the MATLAB algorithm structure of the numerical solution of the fourth-order fixed-step Runge-Kutta and Lorenz models, discusses the error control in the case of variable step size, and plots the numerical solutions of the Lorenz system based on MATLAB in two-dimensional and three-dimensional space graphics. The x -direction displacement and y -direction displacement data are extracted from the Lorenz equation as iterative samples of the model, the regression curve obtained after iteration has a slope of 0.996, and the iterative regression model reflects the basic characteristics of the data well. This method presents the basic idea of numerical solution verification within acceptable error limits. For solving engineering problems with differential equations as mathematical models, an effective numerical solution method is provided, and further discussion on the numerical solutions of partial differential equations is of great significance.

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Section: Literature Reviewmentioning

confidence: 99%

Indifference Computer Experiment for Mathematical Identification of Two Variables

2022

Wireless Communications and Mobile Computing

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Enhanced bond strength prediction in corroded reinforced concrete using optimized ML models

Nguyen,

Trinh,

2024

Structures

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Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

Cited by 2 publications

References 44 publications

Indifference Computer Experiment for Mathematical Identification of Two Variables

Indifference Computer Experiment for Mathematical Identification of Two Variables

Enhanced bond strength prediction in corroded reinforced concrete using optimized ML models

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