Spectral estimation from simulations via sketching
Spectral estimation from simulations via sketching
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DOI：
10.1016/j.jcp.2021.110686
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发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Zhishen Huang;Stephen Becker
中科院分区：
文献类型：
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作者：
Zhishen Huang;Stephen Becker
草图是一种随机降维方法，它保留了数据的几何结构，并在高维回归，低秩近似和图稀疏化中有应用。在这项工作中，我们表明，草图可以用来压缩模拟数据，仍然准确地估计时间自相关和功率谱密度。对于给定的压缩比，精度比使用先前已知的方法高得多。除了提供理论上的保证，我们适用于素描的分子动力学模拟甲醇，发现光谱密度的估计是90%的准确性，仅使用10%的数据。
Sketching is a stochastic dimension reduction method that preserves geometric structures of data and has applications in high-dimensional regression, low rank approximation and graph sparsification. In this work, we show that sketching can be used to compress simulation data and still accurately estimate time autocorrelation and power spectral density. For a given compression ratio, the accuracy is much higher than using previously known methods. In addition to providing theoretical guarantees, we apply sketching to a molecular dynamics simulation of methanol and find that the estimate of spectral density is 90% accurate using only 10% of the data.