Mixing Artificial and Natural Intelligence: From Statistical Mechanics to AI and Back to Turbulence
This is an incremental review discussing potential applications of existing AI methods to turbulence studies in fluid dynamics.
The paper examines how AI, particularly diffusion models from statistical mechanics, can advance turbulence research by developing reduced Lagrangian models through deep neural networks, potentially leading to deeper insights in both fields.
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.