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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…
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…
Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement
Shengyu Chen, Tianshu Bao, Peyman Givi +2
Simulating turbulence is critical for many societally important applications in aerospace engineering, environmental science, the energy industry, and biomedicine. Large eddy simul…
Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks
Shengyu Chen, Shervin Sammak, Peyman Givi +2
Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alte…