Abstract
In this study, a new structure for the septic B-spline collocation algorithm in n-dimensional is presented as a continuation of generating B-spline functions in n-dimensional to solve mathematical models in n-dimensional. The septic B-spline collocation algorithm is displayed in three forms: one dimensional, two dimensional, and three dimensional. In various domains, these constructs are essential for solving mathematical models. The effectiveness and correctness of the suggested method are demonstrated using a few two- and three-dimensional test problems. The proposed new structure provides better results than other methods because it deals with a larger number of points than the field. To create comparisons, we use different numerical approaches accessible in the literature.
1 Introduction
Many researchers have solved some mathematical models in different dimensions using some analytical and numerical methods such as the (3+1)-dimensional Date–Jimbo–Kashiwara–Miwa equation [1], (3+1)-dimensional Boiti–Leon–Manna–Pempinelli equation [2], (2+1)-dimensional Schrödinger’s hyperbolic equation [3], and non-Newtonian fluid models [4,5]. Most mathematical models in several fields, such as fluid mechanics and physics, are challenging to deal with analytically, prompting some researchers to consider numerical solutions. The finite differences approach, as seen in previous studies [6,7], is one of the strategies used in solving n-dimensional models. Various academics have also attempted to adapt some approaches for solving mathematical models in one dimension to solving models in n-dimensional, such as spectral methods [8,9]. However, most nonlinear models were challenging to solve using spectral approaches. Gardner and Gardner studied a two-dimensional bi-cubic B-spline finite element for solving two-dimensional problems [10]. To solve several diverse mathematical models, some researchers used the bi-cubic B-spline finite element method [11–13] and bi-quintic B-spline collocation method [14–16]. Raslan and Ali began to consider generalizing all types of B-spline functions. They talked about n-dimensional quadratic B-splines [17], new structure formulations for the cubic B-spline collocation method in three and four dimensional [18], construction of extended cubic B-splines in n-dimensional for solving n-dimensional partial differential equations [19], and a new structure for n-dimensional trigonometric cubic B-spline functions for solving n-dimensional partial differential equations [20]. The B-spline collocation method and other methods have been used in many articles to solve many mathematical models such as quintic B-splinemethod [21], cubic B-spline method [22,23], and novel collocation techniques [24–26].
The idea of solving mathematical models in different dimensional remains an idea that haunts most researchers in various fields. Although in previous articles we have presented solutions to these problems by generalizing B-spline collocation functions, we are continuing with these generalizations to deal with models that contain ranks higher than the fifth degree. In this article, we present a generalization of the septic B-spline function, where this method can deal with equations of the seventh order and below.
The structure of this article is as follows: In Section 2, n-dimensional septic B-spline formulas are presented. In Section 3, the numerical outcomes are presented. Section 4 introduces numerical examples. Finally, the conclusion of this work is presented.
2 n-dimensional septic B-spline functions
In this section, we present the n-dimensional septic B-splines.
2.1 One-dimensional septic B-spline [27,28]
Let
where
We use (1) and (2) with substitution by collection points to find
The aforementioned analysis yields the following theorem.
2.2 Two-dimensional septic B-spline
This subsection shows the formula for a two-dimensional septic B-spline on a rectangular grid divided into regular rectangular finite elements on both sides.
where
Moreover,
The aforementioned analysis yields the following theorem.
2.3 Three-dimensional septic B-spline
Now, we obtain the septic B-spline in three measurements approximates on a framework divided up into limited components of sides
where
Also,
The aforementioned analysis yields the following theorem:
3 Numerical outcomes
Now, to determine whether or not this method, which was developed by presenting its constructions in n-dimensional space, is correct and effective. In this section, we provide several numerical examples in various dimensions to demonstrate the accuracy of this method. In addition to the comparison of our results with those previously obtained, we also exhibit some of the obtained figures. We should point out that all of the examples were created using the Mathematica 12.1 package and ran on a standard computer (Intel(R) core(TM) i7-351U, CPU@1.90 Hz 2.40 GHz).
The first test problem: [8,9,13,17,18,29]
We take the two-dimensional problem in the following form:
The following is the exact solution to that problem:
We take the boundary conditions to the third problem in the following form:
By substituting from (5)–(7) into (13) with (15), we obtain the numerical results as in Table 1.
Numerical results for the third issue are available at
|
|
Numerical results | Exact results | Absolute error |
|---|---|---|---|
| 0.2 | ‒0.028320 | ‒0.028320 |
|
| 0.4 | ‒0.045822 | ‒0.045822 |
|
| 0.6 | ‒0.045822 | ‒0.045822 |
|
| 0.8 | ‒0.028320 | ‒0.028320 |
|
The results of the two-dimensional septic B-spline approach at
Maximum absolute error based on the approach used to solve the problem
| The proposed method | Cubic B-spline approach [18] | Quadratic B-spline approach [17] | MCBDQM approach [13] | Spline-based DQM approach [29] | Haar wavelet approach [8] | SCA based on Haarwavelets [9] |
|---|---|---|---|---|---|---|
|
|
|
|
|
|
|
|
Now, we show the numerical results and absolute errors at

The graph of numerical results and absolute error at

The graph of numerical results at

Three-dimensional graph for numerical results.
The second test problem:
We take the two-dimensional nonlinear problem in the following form:
where
The following is the exact solution to that problem:
We take the boundary conditions to the third problem in the following form:
By substituting from (5)–(7) into (16) with (19), we obtain the numerical results as in Table 3.
Numerical results for the third issue are available at
|
|
Numerical results | Exact results | Absolute error |
|---|---|---|---|
| 0.2 | 0.079328 | 0.079371 |
|
| 0.4 | 0.105922 | 0.105956 |
|
| 0.6 | 0.094261 | 0.094287 |
|
| 0.8 | 0.055914 | 0.055931 |
|
The results of the two-dimensional septic B-spline approach at

The graph of numerical results and absolute error at

The graph of numerical results at

Three-dimensional graph for numerical results.
The third test problem: [17,18]
We take the second test problem in the three-dimensional in the following form:
where
The exact solution to that problem is given as follows:
We take the boundary conditions to the fourth problem in the following form:
By substituting from (9)–(12) into (20) with (23), we obtain the numerical results as in Table 4.
Numerical results for test problem at
|
|
Numerical solution | Exact solution | Absolute error | Quadratic B-spline method [17] | Cubic B-spline method [18] |
|---|---|---|---|---|---|
| 0.1 | 0.016868 | 0.0168984 |
|
|
|
| 0.2 | 0.033142 | 0.0332012 |
|
|
|
| 0.3 | 0.048073 | 0.0481595 |
|
|
|
| 0.4 | 0.060716 | 0.0608280 |
|
|
|
| 0.5 | 0.069891 | 0.0700264 |
|
|
|
| 0.6 | 0.074136 | 0.0742955 |
|
|
|
| 0.7 | 0.071658 | 0.0718456 |
|
|
|
| 0.8 | 0.060271 | 0.0604965 |
|
|
|
| 0.9 | 0.050423 | 0.0376082 |
|
|
|
Table 4 shows a comparison of our results with those obtained using quadratic B-spline and cubic B-spline approaches with meshes of

The graph of numerical results and absolute error at

Three-dimensional graph for numerical results.
The fourth test problem: [9,20]
We take the test problem in the three-dimensional in the following form:
The following is the exact solution to that problem:
We take the boundary conditions to the third problem in the following form:
By substituting from (9)–(12) into (24) with (26), we obtain the numerical results as in Table 5.
Numerical results for the test problem are available at
|
|
Numerical results | Exact results | Absolute error | Maximum absolute error of our method | Maximum absolute error [20] | Maximum absolute error [9] |
|---|---|---|---|---|---|---|
| 0.2 |
|
|
|
|
|
|
| 0.4 |
|
|
|
— | — | — |
| 0.6 |
|
|
|
— | — | — |
| 0.8 |
|
|
|
— | — | — |
The results of the three-dimensional septic B-spline approach using mesh

The graph of numerical results and absolute error at

Three-dimensional graph for numerical results.
4 Conclusion
By the end of this study, we may have made a significant contribution to addressing some of the problems that most academics in various fields have when dealing with n-dimensional mathematical models. The study object is crucial, and we believe that the majority of academics are eagerly anticipating the results. We noted how difficult it is for researchers to cope with these models as the dimension expands after seeing various scholars present their discoveries on partial differential equation solutions in one-, two-, and three-dimensional. As a result, we decided to extend the basic B-spline method, which had hitherto only been used to solve one-dimensional mathematical problems, to two- and three-dimensional. To assess the correctness and efficacy of the developed schemes, we used numerical examples of various dimensions. When the numerical results are compared to the actual solution, we see that the formulas found are effective. From this perspective, we believe that a significant contribution has been made toward addressing problems involving partial differential equations in many dimensions. The proposed new structure provides accurate results than other methods because it deals with a larger number of points than the field. We would generalize a few other B-spline shapes to serve as solutions to n-dimensional differential equations as part of our long-term research.
Acknowledgement
The researchers would to like acknowledge the deanship of scientific research, Taif University, for funding this work.
-
Funding information: Not applicable.
-
Author contributions: The authors declare that the study was realized in collaboration with equal responsibility. All authors read and approved the final manuscript.
-
Conflict of interest: The authors declare that they have no competing interests.
-
Data availability statement: Data sharing was not applicable to this article as no datasets were generated or analyzed during this study.
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© 2023 the author(s), published by De Gruyter
This work is licensed under the Creative Commons Attribution 4.0 International License.
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- Research on nonlinear tracking and evaluation of sports 3D vision action
- Analysis of bridge vibration response for identification of bridge damage using BP neural network
- Numerical analysis of vibration response of elastic tube bundle of heat exchanger based on fluid structure coupling analysis
- Establishment of nonlinear network security situational awareness model based on random forest under the background of big data
- Research and implementation of non-linear management and monitoring system for classified information network
- Study of time-fractional delayed differential equations via new integral transform-based variation iteration technique
- Exhaustive study on post effect processing of 3D image based on nonlinear digital watermarking algorithm
- A versatile dynamic noise control framework based on computer simulation and modeling
- A novel hybrid ensemble convolutional neural network for face recognition by optimizing hyperparameters
- Numerical analysis of uneven settlement of highway subgrade based on nonlinear algorithm
- Experimental design and data analysis and optimization of mechanical condition diagnosis for transformer sets
- Special Issue: Reliable and Robust Fuzzy Logic Control System for Industry 4.0
- Framework for identifying network attacks through packet inspection using machine learning
- Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning
- Analysis of multimedia technology and mobile learning in English teaching in colleges and universities
- A deep learning-based mathematical modeling strategy for classifying musical genres in musical industry
- An effective framework to improve the managerial activities in global software development
- Simulation of three-dimensional temperature field in high-frequency welding based on nonlinear finite element method
- Multi-objective optimization model of transmission error of nonlinear dynamic load of double helical gears
- Fault diagnosis of electrical equipment based on virtual simulation technology
- Application of fractional-order nonlinear equations in coordinated control of multi-agent systems
- Research on railroad locomotive driving safety assistance technology based on electromechanical coupling analysis
- Risk assessment of computer network information using a proposed approach: Fuzzy hierarchical reasoning model based on scientific inversion parallel programming
- Special Issue: Dynamic Engineering and Control Methods for the Nonlinear Systems - Part I
- The application of iterative hard threshold algorithm based on nonlinear optimal compression sensing and electronic information technology in the field of automatic control
- Equilibrium stability of dynamic duopoly Cournot game under heterogeneous strategies, asymmetric information, and one-way R&D spillovers
- Mathematical prediction model construction of network packet loss rate and nonlinear mapping user experience under the Internet of Things
- Target recognition and detection system based on sensor and nonlinear machine vision fusion
- Risk analysis of bridge ship collision based on AIS data model and nonlinear finite element
- Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique
- Adaptive fuzzy extended state observer for a class of nonlinear systems with output constraint