异常耐药的物理信息的神经网络
D H G Duarte1,2, P D S de Lima1,3, J M de Araújo1
1Universidade Federal do Rio Grande do Norte, Departamento de Física Teórica e Experimental, 59078-970 Natal-RN, Brazil.
Physical review. E
|March 19, 2025
概括
我们使用Tsallis统计学开发了一个异常耐药的物理信息神经网络 (OrPINN). 这种强大的OrPINN可以提高动态问题的解决准确性,即使有来自异常值的显著数据损坏.
科学领域:
- 计算物理学的计算物理.
- 机器学习应用程序 机器学习应用程序
- 数据科学是数据科学.
背景情况:
- 基于物理学的神经网络 (PINN) 是先进的机器学习工具,用于使用物理定律和数据解决动态问题.
- 测量异常值可以严重降低PINN解决方案的准确性.
- 对噪音数据的稳定性对于可靠的科学机器学习模型至关重要.
研究的目的:
- 开发一种基于物理学的新型神经网络,能够抵抗测量数据中的异常值.
- 在存在损坏数据的情况下,提高PINN解决方案的准确性和可靠性.
- 评估拟议方法在波动力学问题上的性能.
主要方法:
- 构建一个抗异常值的PINN (OrPINN) 框架.
- 将Tsallis统计数据集成到PINN损失函数中以减权异常值.
- 在声学和线性弹性波传播动力学上测试OrPINN.
- 在不同程度的数据异常腐败下进行系统调查.
主要成果:
- 对于异常数据,OrPINN显示出显著的稳定性.
- 与标准PINN相比,提高了声学和线性弹性波动动力学的解决方案的准确性.
- 即使具有高度损坏的输入数据集,也保持了有效的性能.
- 验证Tsallis统计方法用于物理信息学习中的异常值缓解.
结论:
- 拟议的OrPINN有效地处理观察数据中的异常值.
- 扎利斯统计提供了一个强大的统计基础,用于对异常耐药的科学机器学习.
- OrPINN提供了一种可靠的方法,用于动态建模与现实世界,杂的实验数据.
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