稳定适应性训练用于在声波传播中的物理信息神经网络
Márcio Marques1, Leonardo Mendonça1, Arthur Bizzi2
1Instituto de Matemática Pura e Aplicada, Rio de Janeiro, Brazil.
JASA express letters
|November 6, 2025
概括
基于物理学的神经网络 (PINNs) 通过使用适应性采样和吸收边界来改善水下声学模拟. 这一策略增强了训练稳定性和趋同性,用于准确的波传播建模.
科学领域:
- 计算物理学的计算物理.
- 声学 声学 在声学方面
- 机器学习是机器学习.
背景情况:
- 基于物理学的神经网络 (PINNs) 显示出科学模拟的前景.
- 水下声学模拟面临着面临大领域和融合问题的挑战.
- 现有的PINN方法在计算域大小和微不足道的解决方案趋同方面扎.
研究的目的:
- 加强PINNs在水下声学中的应用.
- 为了应对大型计算领域的挑战,并在PINN培训中趋同到微不足道的解决方案.
- 开发一种更稳定,更准确的声波传播模拟的策略.
主要方法:
- 将自适应域采样与PINN培训的吸收边界条件相结合.
- 动态地将计算精力集中在局部声能区域上.
- 利用吸收边界来稳定训练过程.
主要成果:
- 提出的方法显著提高了PINN培训的稳定性.
- 在数值实验中观察到增强的收率.
- 该策略导致更准确,更可靠的波传播模拟.
结论:
- 适应域采样和吸收边界条件有效地解决了水下声学中的PINN限制.
- 开发的策略为复杂的声学模拟提供了一个强大的方法.
- 这项工作为使用PINNs更有效,更精确的水下声学建模铺平了道路.
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