Renormalization group flow, optimal transport, and diffusion-based generative model.

Diffusion-based generative models represent a forefront direction in generative artificial intelligence (AI) research today. Recent studies in physics have suggested that the renormalization group (RG) can be conceptualized as a diffusion process. This insight motivates us to develop a diffusion-based generative model by reversing the momentum-space RG flow.

Hydrodynamic advantages of in-line schooling.

Fish benefit energetically when swimming in groups, which is reflected in lower tail-beat frequencies for maintaining a given speed. Recent studies further show that fish save the most energy when swimming behind their neighbor such that both the leader and the follower benefit. However, the mechanisms underlying such hydrodynamic advantages have thus far not been established conclusively.