通过对投影依赖输出的非线性解码来改进降低顺序模型
Kamila Zdybał1,2, Alessandro Parente1,2, James C Sutherland3
1Université Libre de Bruxelles, École Polytechnique de Bruxelles, Aero-Thermo-Mechanics Laboratory, Brussels, Belgium.
Patterns (New York, N.Y.)
|November 30, 2023
概括
改进数据驱动的减少顺序模型 (ROM) 需要更好的数据预测. 本研究使用神经网络来增强低维数据表示,最大限度地减少错误,并提高复杂系统的ROM精度.
科学领域:
- 计算科学是一种计算科学.
- 数据科学是数据科学.
- 机器学习是机器学习.
背景情况:
- 数据驱动的减少顺序模型 (ROM) 对于模拟复杂的动态系统至关重要.
- 低维数据投影的拓质量差,妨碍了ROM的准确性,造成了重叠,扭曲和感兴趣的数量 (QoI) 的坡等问题.
研究的目的:
- 引入一种使用编码器-解码器神经网络的新型维度减小技术.
- 为了提高ROM性能,提高低维数据投影的拓质量.
- 为了最大限度地减少在缩小维度内的QoI表示中的非唯一性和梯度.
主要方法:
- 采用编码器-解码器神经网络架构来减少维度.
- 使用投影依赖的 QoI 的非线性解码,将它们嵌入到缩小维度的过程中.
- 将数据投影 (编码) 的准确性与 QoI 的非线性重建 (解码) 联系起来.
主要成果:
- 为复杂的多尺度和多物理数据集实现了改进的低维表示.
- 尽量减少在投影上的QoI表示中的非唯一性和梯度.
- 证明了由此产生的减少顺序模型的增强预测准确性.
结论:
- 在维度缩减中,QI的非线性解码显著改善了数据表示.
- 这种方法增强了数据驱动的ROM对复杂动态系统的预测能力.
- 这些发现在流体动力学,等离子体物理学和神经科学等领域具有广泛的适用性.
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