数据驱动的流波通过深度学习为聚焦和可变系数非线性施罗丁格方程提供解决方案
Jiuyun Sun1, Huanhe Dong1, Mingshuo Liu1
1College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China.
Chaos (Woodbury, N.Y.)
|July 19, 2024
概括
这项研究使用深度学习,特别是物理信息记忆网络 (PIMNs),准确地解决非线性施罗丁格方程中的流波解决方案. 该方法有效地捕捉了复杂的非线性动态,在解决微分方程方面推进了AI.
科学领域:
- 非线性动力学是一种非线性动力学.
- 计算物理 计算物理
- 人工智能的人工智能
背景情况:
- 流波是非线性系统中的极端振幅事件.
- 非线性施罗丁格方程 (NLS) 模型各种波浪现象,包括流波.
- 解决复杂的微分方程往往需要先进的数值方法.
研究的目的:
- 调查以数据驱动的流波解决方案,用于聚焦和可变系数NLS方程.
- 应用物理信息记忆网络 (PIMNs) 来解决这些方程.
- 分析网络参数对解决方案准确性的影响.
主要方法:
- 使用物理信息记忆网络 (PIMNs) 进行数据驱动的方法.
- 为聚焦NLS方程解决一级和二级流波解决方案.
- 解决变量系数NLS方程的三个变形的流波解决方案.
- 检查优化算法,网络结构和网格大小的影响.
主要成果:
- PIMNs成功地捕获了流波解决方案的非线性特征.
- 在解决标准和变形的流波场景方面表现出高精度.
- 数字实验证实了深度学习方法的有效性.
结论:
- PIMNs是准确解决非线性施罗丁格方程的强大工具.
- 这种深度学习方法为了解流波动态提供了巨大的潜力.
- 该研究强调了人工智能在解决复杂的部分微分方程中的进步.
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