57 citations · 137 across the 11 of their papers we have counts for
Showing 2026Show all
3 papers · 1 filter
cs.LG2026
Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It
Yilong Dai, Shaswata Mitra, Raj Patel +7
Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term pointwise accuracy followed by…
physics.flu-dyn2026
TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning
Yilong Dai, Yiming Sun, Yiheng Chen +4
Turbulence is a central testbed for machine learning on physical dynamics because its governing laws are known exactly. However, most existing studies remain in 2D, while 3D turbul…
physics.flu-dyn2026
PEST: Physics-Enhanced Swin Transformer for 3D Turbulence Simulation
Yilong Dai, Shengyu Chen, Xiaowei Jia +2
Accurate simulation of turbulent flows is fundamental to scientific and engineering applications. Direct numerical simulation (DNS) offers the highest fidelity but is computational…