通过物理信息神经计算,从噪声观测中回溯流体动力学的过去

IF 2.4 3区 物理与天体物理 Q1 Mathematics
Jaemin Seo
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引用次数: 0

摘要

由于数值和物理方面的挑战,特别是当观测数据因不可避免的噪声、分辨率限制或未知因素而失真时,重建观测流体的过去一直被认为是一个难以解决的问题。当采用传统的差分方案来重建过去时,计算往往会变得非常不稳定,或者在距离扭曲和噪声观测数据几个后向时间步长内就会发散。虽然最近针对逆问题开发出了一些技术,如邻接求解器和监督学习,但在时间反演模拟时,这些技术也无法抵御观测误差。在此,我们提出,通过使用物理信息神经计算,鲁棒的时间反演流体模拟是可能的。通过寻找一个既能满足给定物理和观测条件,又能允许误差的解决方案,它能从嘈杂的观测结果中重建最有可能的过去。我们的工作展示了在冲击、不稳定性、爆炸和磁流体涡旋等极端流体情况下的时间倒流。这有可能被应用于追溯星际演化和确定聚变等离子体不稳定性的起源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing

Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing
Reconstructing the past of observed fluids has been known as an ill-posed problem due to both numerical and physical challenges, especially when observations are distorted by inevitable noise, resolution limits, or unknown factors. When employing traditional differencing schemes to reconstruct the past, the computation often becomes highly unstable or diverges within a few backward time steps from the distorted and noisy observation. Although several techniques have been recently developed for inverse problems, such as adjoint solvers and supervised learning, they are also unrobust against errors in observation when there is time-reversed simulation. Here we present that by using physics-informed neural computing, robust time-reversed fluid simulation is possible. By seeking a solution that closely satisfies the given physics and observations while allowing for errors, it reconstructs the most probable past from noisy observations. Our work showcases time rewinding in extreme fluid scenarios such as shock, instability, blast, and magnetohydrodynamic vortex. Potentially, this can be applied to trace back the interstellar evolution and determining the origin of fusion plasma instabilities.
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来源期刊
Physical review. E
Physical review. E 物理-物理:流体与等离子体
CiteScore
4.60
自引率
16.70%
发文量
0
审稿时长
3.3 months
期刊介绍: Physical Review E (PRE), broad and interdisciplinary in scope, focuses on collective phenomena of many-body systems, with statistical physics and nonlinear dynamics as the central themes of the journal. Physical Review E publishes recent developments in biological and soft matter physics including granular materials, colloids, complex fluids, liquid crystals, and polymers. The journal covers fluid dynamics and plasma physics and includes sections on computational and interdisciplinary physics, for example, complex networks.
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