深潜力模型:贝叶斯深度学习的基于ODE的过程卷曲
Thomas Baldwin-McDonald1, Xinxing Shi1, Mingxin Shen1
1Department of Computer Science, University of Manchester, Oxford Road, Manchester, M13 9PL UK.
概括
我们介绍了深潜力模型 (DLFM),这是一个用于建模非线性动态系统的新方法. 这种以物理学为基础的高斯过程模型有效量化不确定性,并捕捉复杂的时间序列动态.
科学领域:
- 机器学习 机器学习
- 动态系统建模 动态系统建模
- 不确定性定量化 不确定性定量化
背景情况:
- 建模高度非线性动态系统与强大的不确定性量化是复杂的.
- 现有的方法往往需要针对特定问题的设计.
- 为了更广泛的适用性,需要一种域异的方法.
研究的目的:
- 为非线性动态系统引入一个域不可知模型.
- 开发一个深度高斯过程与物理学知情的内核.
- 在复杂系统建模中实现可靠的不确定性量化.
主要方法:
- 开发了深潜力力模型 (DLFM),一种深度高斯过程.
- 从普通微分方程中获得的内置物理信息的内核.
- 使用了两种配方:重量空间和变化诱导点.
- 采用双倍随机变化推理用于模型近似.
主要成果:
- DLFM有效地捕获高度非线性,多输出时间序列数据中的动态.
- 在回归任务上实现了与非物理知情模型可比的性能.
- 确定了诱导点对外推算能力的负面影响.
结论:
- DLFM为模拟复杂的动态系统提供了一种强大的,域异的解决方案.
- 基于物理的内核增强了模型捕捉系统动态和量化不确定性的能力.
- 需要进一步的研究来优化外推性能.
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