Variational encoding of complex dynamics.
Variational encoding of complex dynamics.
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DOI：
10.1103/physreve.97.062412
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发表时间：
2018-06
期刊：
影响因子：
--
通讯作者：
Pande VS
中科院分区：
文献类型：
--
作者：
Hernández CX;Wayment-Steele HK;Sultan MM;Husic BE;Pande VS
通常，对时间依赖的化学和生物物理系统的分析产生高维时间序列数据，对于这些数据，很难解释哪些个体特征是最显著的。虽然我们小组和其他人最近的工作已经证明了时滞协变量模型在研究此类系统中的实用性，但线性假设可以将固有的非线性动力学压缩为几个特征分量。最近在深度学习领域的工作导致了变分自动编码器（VAE）的发展，它能够将复杂的数据集压缩成更简单的流形。我们提出了使用一个时间滞后的VAE，或变分动态编码器（VDE），以减少复杂的，非线性的过程，以一个单一的嵌入高保真的底层动态。我们演示了如何的VDE是能够捕捉非平凡的动态在各种例子中，包括布朗动力学和原子蛋白质折叠。此外，我们展示了一种方法，用于分析的VDE模型，显着映射的启发，以确定什么样的功能选择的VDE模型来描述动态。VDE是应用深度学习技术更准确地建模和解释复杂生物物理学的重要一步。
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged covariate models to study such systems, linearity assumptions can limit the compression of inherently nonlinear dynamics into just a few characteristic components. Recent work in the field of deep learning has led to the development of the variational autoencoder (VAE), which is able to compress complex datasets into simpler manifolds. We present the use of a time-lagged VAE, or variational dynamics encoder (VDE), to reduce complex, nonlinear processes to a single embedding with high fidelity to the underlying dynamics. We demonstrate how the VDE is able to capture nontrivial dynamics in a variety of examples, including Brownian dynamics and atomistic protein folding. Additionally, we demonstrate a method for analyzing the VDE model, inspired by saliency mapping, to determine what features are selected by the VDE model to describe dynamics. The VDE presents an important step in applying techniques from deep learning to more accurately model and interpret complex biophysics.