双编码器物理信息变分自编码器在噪声测量下的鲁棒正向和逆SDE求解

IF 3.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Lin Wang, Min Yang
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引用次数: 0

摘要

随机微分方程(SDEs)的实际测量结果经常受到异质或高强度噪声的破坏,这对基于深度学习的求解器提出了重大挑战。在这项研究中,我们提出了一个双编码器物理通知变分自动编码器(DE-PIVAE)框架,明确地从物理变量中分离潜在噪声。与以前的单编码器PI-VAE方法不同,我们的模型使用一个编码器来学习底层干净的物理信号,并使用一个额外的噪声编码器从有限的传感器数据中捕获测量噪声的统计特性。该框架将物理约束纳入到端到端训练中,能够更准确地重建噪声观测下的真实系统状态。我们考虑了广泛的噪声场景,包括不同的噪声类型,不同的噪声强度,部分传感器损坏和噪声分布不规范。实验结果表明,DE-PIVAE算法的性能优于基线算法,在高噪声条件下,其优势更加明显。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dual-Encoder Physics-Informed Variational Autoencoders for robust forward and inverse SDE solving under noisy measurements
Real-world measurements for stochastic differential equations (SDEs) are often corrupted by heterogeneous or high-intensity noise, which poses significant challenges for deep learning-based solvers. In this study, we propose a Dual-Encoder Physics-Informed Variational AutoEncoder (DE-PIVAE) framework that explicitly disentangles latent noise from physical variables. Unlike previous single-encoder PI-VAE approaches, our model employs one encoder to learn the underlying clean physical signal and an additional noise encoder to capture the statistical properties of measurement noise from limited sensor data. The framework incorporates physical constraints into end-to-end training and enables more accurate reconstruction of the true system states under noisy observations. We consider a broad spectrum of noisy scenarios, including different noise types, varying noise intensities, partial sensor corruption, and noise distribution misspecification. Experimental results show that DE-PIVAE achieves superior performance compared to baseline solvers, with its advantage becoming more pronounced under higher noise.
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来源期刊
CiteScore
7.20
自引率
9.10%
发文量
852
审稿时长
6.6 months
期刊介绍: Physica A: Statistical Mechanics and its Applications Recognized by the European Physical Society Physica A publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems by studying the statistical properties of their microscopic constituents. Applications of the techniques of statistical mechanics are widespread, and include: applications to physical systems such as solids, liquids and gases; applications to chemical and biological systems (colloids, interfaces, complex fluids, polymers and biopolymers, cell physics); and other interdisciplinary applications to for instance biological, economical and sociological systems.
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