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Computer Science > Machine Learning

Title: Mixing Artificial and Natural Intelligence: From Statistical Mechanics to AI and Back to Turbulence

Authors: Michael (Misha) Chertkov
Abstract: The paper reflects on the future role of 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.
Comments: 35 pages, 9 figures
Subjects: Machine Learning (cs.LG); Statistical Mechanics (cond-mat.stat-mech); Artificial Intelligence (cs.AI); Fluid Dynamics (physics.flu-dyn)
Cite as: arXiv:2403.17993 [cs.LG]
  (or arXiv:2403.17993v1 [cs.LG] for this version)

Submission history

From: Michael Chertkov [view email]
[v1] Tue, 26 Mar 2024 12:45:52 GMT (23026kb,D)

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