人工智能与自然智能的混合：从统计力学到人工智能，再回到湍流

IF 2 3区物理与天体物理 Q2 PHYSICS, MATHEMATICAL

Journal of Physics A: Mathematical and Theoretical Pub Date : 2024-08-01 DOI:10.1088/1751-8121/ad67bb

Michael (Misha) Chertkov

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引用次数: 0

摘要

本文反思了人工智能（AI）在科学研究中的未来作用，特别关注湍流研究，并探讨了人工智能的演变，尤其是通过植根于非平衡统计力学的扩散模型。它强调了人工智能通过创新性地使用深度神经网络，对推进简化的拉格朗日湍流模型所产生的重大影响。此外，论文还回顾了人工智能在湍流研究中的其他各种应用，并概述了人工智能和统计流体力学同时发展所面临的潜在挑战和机遇。这一讨论为人工智能与湍流研究错综复杂地交织在一起的未来奠定了基础，从而为这两个领域带来更深刻的见解和进步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Mixing artificial and natural intelligence: from statistical mechanics to AI and back to turbulence

The paper reflects on the future role of artificial intelligence (AI) in scientific research, with a special focus on turbulence studies, and examines the evolution of AI, particularly through Diffusion Models rooted in non-equilibrium statistical mechanics. It underscores the significant impact of AI on advancing reduced, Lagrangian models of turbulence through innovative use of Deep Neural Networks. Additionally, the paper reviews various other AI applications in turbulence research and outlines potential challenges and opportunities in the concurrent advancement of AI and statistical hydrodynamics. This discussion sets the stage for a future where AI and turbulence research are intricately intertwined, leading to more profound insights and advancements in both fields.

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来源期刊

Journal of Physics A: Mathematical and Theoretical 物理-物理：数学物理

CiteScore

4.10

自引率

14.30%

发文量

542

审稿时长

1.9 months

期刊介绍： Publishing 50 issues a year, Journal of Physics A: Mathematical and Theoretical is a major journal of theoretical physics reporting research on the mathematical structures that describe fundamental processes of the physical world and on the analytical, computational and numerical methods for exploring these structures.