An improved water strider algorithm for solving the inverse Burgers Huxley equation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04274644%3A_____%2F24%3A%230001159" target="_blank" >RIV/04274644:_____/24:#0001159 - isvavai.cz</a>

Result on the web

<a href="https://www.nature.com/articles/s41598-024-78907-0" target="_blank" >https://www.nature.com/articles/s41598-024-78907-0</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-024-78907-0" target="_blank" >10.1038/s41598-024-78907-0</a>

Result language

angličtina

Original language name

An improved water strider algorithm for solving the inverse Burgers Huxley equation

Original language description

In this paper, we introduce an improved water strider algorithm designed to solve the inverse form of the Burgers-Huxley equation, a nonlinear partial differential equation. Additionally, we propose a physics-informed neural network to address the same inverse problem. To demonstrate the effectiveness of the new algorithm and conduct a comparative analysis, we compare the results obtained using the improved water strider algorithm against those derived from the original water strider algorithm, a genetic algorithm, and a physics-informed neural network with three hidden layers. Solving the inverse form of nonlinear partial differential equations is crucial in many scientific and engineering applications, as it allows us to infer unknown parameters or initial conditions from observed data. This process is often challenging due to the complexity and nonlinearity of the equations involved. Meta-heuristic algorithms and neural networks have proven to be effective tools in addressing these challenges. The numerical results affirm the efficiency of our proposed method in solving the inverse form of the Burgers-Huxley equation. The best results were obtained using the improved water strider algorithm and the physics-informed neural network with 10,000 iterations. With this iteration count, the mean absolute error of these algorithms is O(10−4). Additionally, the improved water strider algorithm is nearly four times faster than the physics-informed neural network. All algorithms were executed on a computing system equipped with an Intel(R) Core(TM) i7-7500U processor and 12.00 GB of RAM, and were implemented in MATLAB.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

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Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

DE - GERMANY

Number of pages

16

Pages from-to

1-16

UT code for WoS article

001361325400013

EID of the result in the Scopus database

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