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WF-PINNs:通过使用弱形式物理信息的神经网络解决斜坡汉堡方程的前向和反向问题
Xianke Wang1, Shichao Yi2,3,4, Huangliang Gu5
1School of Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China.
Scientific reports
|November 18, 2025
概括
本研究介绍了一个弱形式物理信息神经网络 (WF-PINN),用于解决复杂的流体动力学问题. WF-PINNs提高了冲击波建模和反向问题的稳定性和准确性.
科学领域:
- 计算流体动力学 计算流体动力学
- 应用数学 应用数学 应用数学
- 机器学习用于PDE.
背景情况:
- 汉堡方程的数值解决方案面临着的坡度和冲击波带来的挑战.
- 冲击波建模中的反向问题通常是错误的,阻碍了准确的参数估计.
研究的目的:
- 开发一种强大的数值方法来解决不连续性的保存定律.
- 为了提高物理信息神经网络 (PINNs) 的稳定性和准确性,用于前向和反向问题.
- 为了使精确的冲击捕获和参数识别在流体动力学.
主要方法:
- 实施一个弱形式的物理信息神经网络 (WF-PINN).
- 使用弱形积分配方用于PDE损失以提高训练稳定性.
- 为物理一致的冲击波解决方案强制执行的条件.
- 采用双网络架构用于反向问题,包括用于初始条件重建的辅助网络.
主要成果:
- 与强型PINN相比,WF-PINN显示出更高的准确性和收稳定性.
- 冲击波位置和振幅的精确分辨率.
- 成功识别了未知的初始条件和粘度系数.
- 在高梯度地区增强稳定性.
结论:
- WF-PINNs为解决不连续性PDEs提供了一个统一和可泛化的框架.
- 拟议的方法显著提高了对冲击主导流量的数值稳定性和准确性.
- 这种方法为流体动力学前向模拟和逆向建模提供了强大的工具.
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