通过积极学习进行预测性规模桥梁模拟
Satish Karra1,2, Mohamed Mehana3, Nicholas Lubbers4
1Energy and Natural Resources Security Group, Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA.
Scientific reports
|September 27, 2023
概括
本研究引入了一种主动学习方法,通过将细度分子动力学与粗度水力学相结合来改进计算模拟. 这种方法可以提高复杂系统的物理保真性,例如纳米孔介质和聚变能源研究.
科学领域:
- 计算科学 计算科学
- 多尺度建模多尺度建模
- 积极学习是积极学习.
背景情况:
- 越来越多的计算能力需要先进的方法,超出了粗暴武力模拟.
- 精确建模像纳米孔介质运输和惯性封闭融合等现象,需要结合分子层面的相互作用.
研究的目的:
- 开发一种新的主动学习方法来优化微量级模拟,以告知粗量级水力学.
- 通过桥梁尺度来增强计算模型中的物理忠实性.
主要方法:
- 利用主动学习指导执行局部微量分子动力学计算.
- 预测连续粗度轨迹,以投机地启动新的细度模拟.
- 动态更新基于细度结果的大规模模型.
- 在神经网络模型中量化不确定性.
主要成果:
- 开发了一种集成多尺度模拟的新功能.
- 该方法有效地使用细度数据来增强粗度水力动力学预测.
- 在预测模型中已经解决了不确定性量化问题.
结论:
- 开发的积极学习框架为提高计算模拟的准确性和效率提供了一个强大的策略.
- 这种方法可以在需要多尺度分析的领域进行更精确的预测,例如能源提取和核聚变研究.
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