学习Korteweg-De Vries方程的非线性单一波解决方案,使用新的神经网络算法
1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
本研究引入了一种基于Lie组的新型神经网络,用于模拟Korteweg-De Vries (KdV) 方程. 该方法在使用显著更少的数据来建模非线性波传播方面实现了高精度.
科学领域:
- 计算物理学的计算物理.
- 应用数学 应用数学 应用数学
- 非线性动力学是一种非线性动力学.
背景情况:
- 在非线性和分散介质中波传播带来了重大挑战.
- 科尔特韦格-德弗里斯 (KdV) 方程模拟了各种现象,包括浅水波和等离子波.
- 精确模拟KdV方程对于理解复杂的物理系统至关重要.
研究的目的:
- 开发一种新的,数据效率高的算法,用于解决KdV方程的非线性单一波问题.
- 利用李群方法和神经网络来提高模拟准确度.
- 为了证明拟议方法在捕获KdV方程动态方面的有效性.
主要方法:
- 应用了移动波变换来减少KdV方程的维度.
- 基于Lie组的神经网络被用于模拟.
- 为了训练神经网络,使用了布罗登-弗莱彻-戈德法尔布-沙诺 (BFGS) 优化方法.
主要成果:
- 拟议的算法在模拟KdV方程时实现了高精度.
- 与传统方法相比,该方法需要的数据要少得多.
- 实验结果验证了基于Lie组的神经网络的有效性和准确性.
结论:
- 基于Lie组的神经网络为研究非线性波传播提供了强大而高效的工具.
- 这种数据效率高的方法有助于精确模拟KdV方程所模拟的复杂现象.
- 该方法对涉及非线性动态的各种领域的应用具有前景.
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