57 citations · 137 across the 11 of their papers we have counts for
12 papers
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…
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…
BrainPuzzle: Hybrid Physics and Data-Driven Reconstruction for Transcranial Ultrasound Tomography
Shengyu Chen, Shihang Feng, Yi Luo +2
Ultrasound brain imaging remains challenging due to the large difference in sound speed between the skull and brain tissues and the difficulty of coupling large probes to the skull…
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…
HOSSnet: an Efficient Physics-Guided Neural Network for Simulating Crack Propagation
Shengyu Chen, Shihang Feng, Yao Huang +4
Hybrid Optimization Software Suite (HOSS), which is a combined finite-discrete element method (FDEM), is one of the advanced approaches to simulating high-fidelity fracture and fra…
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…
Error estimates of numerical methods for the nonlinear Dirac equation in the nonrelativistic limit regime
Weizhu Bao, Yongyong Cai, Xiaowei Jia +1
We present several numerical methods and establish their error estimates for the discretization of the nonlinear Dirac equation in the nonrelativistic limit regime, involving a sma…
A uniformly accurate (UA) multiscale time integrator pseudospectral method for the Dirac equation in the nonrelativistic limit regime
Weizhu Bao, Yongyong Cai, Xiaowei Jia +1
We propose and rigourously analyze a multiscale time integrator Fourier pseudospectral (MTI-FP) method for the Dirac equation with a dimensionless parameter w…
Numerical methods and comparison for the Dirac equation in the nonrelativistic limit regime
Weizhu Bao, Yongyong Cai, Xiaowei Jiao +1
We analyze rigorously error estimates and compare numerically spatial/temporal resolution of various numerical methods for the discretization of the Dirac equation in the nonrelati…