Kavli Affiliate: Jing Wang
| First 5 Authors: Dongyu Luo, Jianyu Wu, Jing Wang, Hairun Xie, Xiangyu Yue
| Summary:
We showcase the plain diffusion models with Transformers are effective
predictors of fluid dynamics under various working conditions, e.g., Darcy flow
and high Reynolds number. Unlike traditional fluid dynamical solvers that
depend on complex architectures to extract intricate correlations and learn
underlying physical states, our approach formulates the prediction of flow
dynamics as the image translation problem and accordingly leverage the plain
diffusion model to tackle the problem. This reduction in model design
complexity does not compromise its ability to capture complex physical states
and geometric features of fluid dynamical equations, leading to high-precision
solutions. In preliminary tests on various fluid-related benchmarks, our
DiffFluid achieves consistent state-of-the-art performance, particularly in
solving the Navier-Stokes equations in fluid dynamics, with a relative
precision improvement of +44.8%. In addition, we achieved relative improvements
of +14.0% and +11.3% in the Darcy flow equation and the airfoil problem with
Euler’s equation, respectively. Code will be released at
https://github.com/DongyuLUO/DiffFluid upon acceptance.
| Search Query: ArXiv Query: search_query=au:”Jing Wang”&id_list=&start=0&max_results=3