3 papers
physics.flu-dyn2026
Turbulence generation and data assimilation in wall-bounded flows with a latent diffusion model
Fabian Steinbrenner, Baris Turan, Hao Teng +1
Wall-bounded turbulent flows are chaotic and multiscale, rendering fast prediction at high Reynolds numbers computationally prohibitive in applications such as wind farms. Classica…
physics.flu-dyn2025
Toward a unified data-driven turbulence model through multi-objective learning
Zhuoran Liu, Haochen Wang, Zhuolin Zhao +1
Turbulence remains one of the last unresolved problems of classical physics and a major bottleneck to accurate flow prediction in climate, aerospace, and energy systems. Industrial…
physics.comp-ph2023
First-principle-like reinforcement learning of nonlinear numerical schemes for conservation laws
Hao-Chen Wang, Meilin Yu, Heng Xiao
In this study, we present a universal nonlinear numerical scheme design method enabled by multi-agent reinforcement learning (MARL). Different from contemporary supervised-learning…