用局部高斯过程替代模型进行分子模拟的加速贝叶斯推理
Brennon L Shanks1, Harry W Sullivan1, Abdur R Shazed1
1Department of Chemical Engineering, University of Utah, Salt Lake City, UT 84112-9202, United States.
Journal of chemical theory and computation
|March 29, 2024
概括
局部高斯过程 (LGPs) 为复杂的物理化学模拟加速贝叶斯推理. 这种方法显著加快了大数据集的不确定性量化,超过了传统方法.
科学领域:
- 物理化学 物理化学
- 计算化学计算化学
- 统计建模 统计建模
背景情况:
- 贝叶斯推理对于不确定性量化至关重要,但计算密集.
- 模拟复杂的物理化学数据,如光谱和散射模式,加剧了计算挑战.
- 现有的方法在高维数据集方面存在困难.
研究的目的:
- 引入局部高斯过程 (LGP) 替代模型,以加速贝叶斯推理.
- 在物理化学模拟中克服计算障碍.
- 为了使复杂的热物理性质能够有效量化不确定性.
主要方法:
- 采用了本地高斯过程 (LGP) 替代模型.
- 开发了一种关于独立变量的线性时间复杂性的方法.
- 在液体虹的辐射分布函数上训练了一名LGP.
主要成果:
- 与分子动力学模拟相比,实现了176万倍的加速.
- 超过了传统的高斯过程的性能,大小为三级.
- 用独立变量证明了LGP时间复杂性的线性缩放.
结论:
- 对于贝叶斯推理来说,LGP是强大的,高效的替代模型.
- 在分子模拟中,LGP显著降低了计算成本.
- 这种方法将贝叶斯推理应用扩展到物理化学中的各种实验数据.
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