基于CNN-LSTM和蒙特卡洛方法的重力水可靠性计算方法的调查
Ming-Wei Li1,2, Jun-Qi Ren1, Jing Geng1
1College of Shipbuilding Engineering, Harbin Engineering University, Harbin, Heilongjiang, China.
Network (Bristol, England)
|December 29, 2024
概括
这项研究引入了一种新的水应力计算方法,该方法结合了深度学习和蒙特卡洛模拟. DS-FEM-CNN-LSTM-MC方法提高了准确性,并减少了非线性动态系统的计算时间.
科学领域:
- 地质技术工程 地质技术工程
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 传统的蒙特卡洛 (MC) 方法用于大应力计算是计算密集且耗时的.
- 非线性动态系统,例如模拟大应力系统,对准确和高效的分析提出了挑战.
- 现有的方法经常在平衡计算精度和速度方面扎.
研究的目的:
- 为了提高大应力计算的准确性和效率.
- 为非线性动态系统开发一种新的计算框架.
- 为了减少与蒙特卡洛模拟相关的计算时间和工作量.
主要方法:
- 使用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 进行非线性水应力模拟,开发了DS-FEM-CNN-LSTM联合预测模型.
- 使用实验设计 (DOE) 方法优化MC模拟的样本点设计.
- 在MC框架内,重量因子和故障表面距离被用作选标准.
主要成果:
- 拟议的DS-FEM-CNN-LSTM-MC方法在与现有技术相比显示出更高的性能.
- 在计算时间消耗和准确性方面都取得了显著的改进.
- 深度学习与MC模拟的整合有效地解决了个别方法的局限性.
结论:
- 该DS-FEM-CNN-LSTM-MC方法为大应力可靠性计算提供了更有效,更准确的方法.
- 这种混合方法为分析复杂的非线性动态系统提供了强大的解决方案.
- 这些发现表明,在推进地质工程中的计算方法方面,有前途的方向.
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