Numerical Solution of Dirichlet Boundary Value Problems using Mesh Adaptive Direct Search Optimization

Muhammad Jalil Ahmad, K. Günel
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Abstract

This study gives a different numerical approach for solving second order differential equation with a Dirichlet boundary condition. Mesh Adaptive Direct Search (MADS) algorithm is adopted to train the feed forward neural network used in this approach. As MADS is a derivative-free optimization algorithm, it helps us to reduce the time-consuming workload in the training stage. The results obtained from this approach are also compared with Generalized Pattern Search (GPS) algorithm.
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基于网格自适应直接搜索优化的Dirichlet边值问题数值解
本文给出了具有狄利克雷边界条件的二阶微分方程的另一种数值解法。该方法采用网格自适应直接搜索(MADS)算法对前馈神经网络进行训练。由于MADS是一种无导数优化算法,它可以帮助我们减少训练阶段的耗时工作量。并与广义模式搜索(GPS)算法进行了比较。
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