6 papers · 1 filter
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…
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…
Matrix Product State Simulation of Reacting Shear Flows
Robert Pinkston, Nikita Gourianov, Hirad Alipanah +3
Direct numerical simulation (DNS) of turbulent reactive flows has been the subject of significant research interest for several decades. Accurate prediction of the effects of turbu…
Tensor networks enable the calculation of turbulence probability distributions
Nikita Gourianov, Peyman Givi, Dieter Jaksch +1
Predicting the dynamics of turbulent fluid flows has long been a central goal of science and engineering. Yet, even with modern computing technology, accurate simulation of all but…
Modeling Continuous Spatial-temporal Dynamics of Turbulent Flow with Test-time Refinement
Shengyu Chen, Peyman Givi, Can Zheng +1
The precise simulation of turbulent flows holds immense significance across various scientific and engineering domains, including climate science, freshwater science, and energy-ef…
Physics-enhanced Neural Operator for Simulating Turbulent Transport
Shengyu Chen, Peyman Givi, Can Zheng +1
The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the develop…