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分析了神经网络模型求解最速降线问题的基本算法思路,利用TensorFowl深度学习框架的神经网络模块对最速降线问题进行了数值求解,给出了具体求解步骤,并讨论了神经网络超参数对求解结果的影响。与传统神经网络应用不同,研究了神经网络模型在变分函数拟合中的应用,为将神经网络等AI大模型技术应用于大学物理及实验教学和研究提供参考和借鉴。
Abstract：The basic algorithmic approach of neural network models for solving the brachistochrone problem was analyzed. The Tensor Fowl deep learning framework's neural network module was utilized to numerically solve the brachistochrone problem, with specific solution steps provided and the impact of neural network hyperparameters on the solution results discussed. Unlike traditional neural network applications, this study explored the application of neural network models in variational function fitting, offering references and insights for applying neural networks and other large AI models to university physics teaching, experiments, and research.
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基本信息:
DOI：10.27024/j.wlygc.2025.10.09.03
中图分类号:TP183
引用信息:
[1]贾小文,范海英.利用神经网络模型算法求解最速降线研究[J].物理与工程,2026,36(02):173-178.DOI:10.27024/j.wlygc.2025.10.09.03.
2026-04-15
2026-04-15