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<p>In this paper, a feed-forward artificial neural network (FFNN) is proposed to analyze the behaviour characterized by nonlinear advection-diffusion-reaction (ADR) equations. This approach uses a trial function that satisfies the initial and boundary conditions and depends on a neural network constructed to approximate the solution of the problem. Since the trial function contains unknown parameters, the solution process must be minimized by using efficient optimization techniques to obtain these parameters. Therefore, in this paper, the gradient descent (GD) and particle swarm optimization (PSO) techniques are proposed to address the minimization issue. The results obtained by combining artificial neural network (ANN) method with the optimization techniques have been compared and the advantages and disadvantages of the problems have been discussed. The results revealed that the proposed ANN techniques have produced accurate and reliable solutions by comparing the exact and available literature. Furthermore, these techniques are economical in terms of computational memory.</p>

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Neural network based techniques for steep behaviour represented by nonlinear advection–diffusion-reaction models

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月26日

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  • Seda Gulen,
  • Murat Sari,
  • Pelin Celenk

摘要

In this paper, a feed-forward artificial neural network (FFNN) is proposed to analyze the behaviour characterized by nonlinear advection-diffusion-reaction (ADR) equations. This approach uses a trial function that satisfies the initial and boundary conditions and depends on a neural network constructed to approximate the solution of the problem. Since the trial function contains unknown parameters, the solution process must be minimized by using efficient optimization techniques to obtain these parameters. Therefore, in this paper, the gradient descent (GD) and particle swarm optimization (PSO) techniques are proposed to address the minimization issue. The results obtained by combining artificial neural network (ANN) method with the optimization techniques have been compared and the advantages and disadvantages of the problems have been discussed. The results revealed that the proposed ANN techniques have produced accurate and reliable solutions by comparing the exact and available literature. Furthermore, these techniques are economical in terms of computational memory.