Optimization functions for neural network-based approximation of Burger’s–Fisher equation: A comparative analysis

This study explores the effectiveness of different optimization functions for approximating the solution of Burger’s–Fisher equation with initial and boundary conditions based on neural networks. It compares and analyzes the performance of nine common optimization functions, emphasizing computational accuracy. Extensive experiments show that the choice of appropriate optimization function significantly influences the performance of neural network-based solvers for approximating the solution of Burger’s–Fisher equation with initial and boundary conditions. The findings provide valuable insights and practical recommendations for researchers applying neural networks to solve Berger's equation in fields such as fluid dynamics and heat transfer.

کلیدواژه ها:

Burger’s–Fisher equation ، Neural Networks ، Optimization functions