Synthetic Lagrangian turbulence by generative diffusion models

IF 18.8 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
T. Li, L. Biferale, F. Bonaccorso, M. A. Scarpolini, M. Buzzicotti
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引用次数: 0

Abstract

Lagrangian turbulence lies at the core of numerous applied and fundamental problems related to the physics of dispersion and mixing in engineering, biofluids, the atmosphere, oceans and astrophysics. Despite exceptional theoretical, numerical and experimental efforts conducted over the past 30 years, no existing models are capable of faithfully reproducing statistical and topological properties exhibited by particle trajectories in turbulence. We propose a machine learning approach, based on a state-of-the-art diffusion model, to generate single-particle trajectories in three-dimensional turbulence at high Reynolds numbers, thereby bypassing the need for direct numerical simulations or experiments to obtain reliable Lagrangian data. Our model demonstrates the ability to reproduce most statistical benchmarks across time scales, including the fat-tail distribution for velocity increments, the anomalous power law and the increased intermittency around the dissipative scale. Slight deviations are observed below the dissipative scale, particularly in the acceleration and flatness statistics. Surprisingly, the model exhibits strong generalizability for extreme events, producing events of higher intensity and rarity that still match the realistic statistics. This paves the way for producing synthetic high-quality datasets for pretraining various downstream applications of Lagrangian turbulence. Modelling the statistical and geometrical properties of particle trajectories in turbulent flows is key to many scientific and technological applications. Li and colleagues introduce a data-driven diffusion model that can generate high-Reynolds-number Lagrangian turbulence trajectories with statistical properties consistent with those of the training set and even generalize to rare, intense events unseen during training.

Abstract Image

Abstract Image

通过生成扩散模型合成拉格朗日湍流
拉格朗日湍流是工程、生物流体、大气、海洋和天体物理学中与分散和混合物理学有关的众多应用和基础问题的核心。尽管在过去 30 年中进行了卓越的理论、数值和实验研究,但没有任何现有模型能够忠实地再现湍流中粒子轨迹所表现出的统计和拓扑特性。我们提出了一种基于最先进扩散模型的机器学习方法,用于生成高雷诺数三维湍流中的单粒子轨迹,从而避免了直接通过数值模拟或实验获取可靠拉格朗日数据的需要。我们的模型展示了在时间尺度上再现大多数统计基准的能力,包括速度增量的胖尾分布、反常幂律和耗散尺度附近增加的间歇性。在耗散尺度以下观察到轻微偏差,特别是加速度和平坦度统计。令人惊讶的是,该模型对极端事件表现出很强的普适性,产生的事件强度更高、更罕见,但仍与现实的统计数据相吻合。这为合成高质量数据集以预训练拉格朗日湍流的各种下游应用铺平了道路。
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来源期刊
CiteScore
36.90
自引率
2.10%
发文量
127
期刊介绍: Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.
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