5 papers
Dual-Domain Deep Learning Method to Accelerate Local Basis Functions Computation for Reservoir Simulation in High-Contrast Porous Media
Peiqi Li, Jie Chen
In energy science, Darcy flow in heterogeneous porous media is a central problem in reservoir sim-ulation. However, the pronounced multiscale characteristics of such media pose sig…
Mixed Precision Orthogonalization-Free Projection Methods for Eigenvalue and Singular Value Problems
Tianshi Xu, Zechen Zhang, Jie Chen +2
Mixed-precision arithmetic offers significant computational advantages for large-scale matrix computation tasks, yet preserving accuracy and stability in eigenvalue problems and th…
Hybrid Two-Stage Reconstruction of Multiscale Subsurface Flow with Physics-informed Residual Connected Neural Operator
Peiqi Li, Jie Chen
The novel neural networks show great potential in solving partial differential equations. For single-phase flow problems in subsurface porous media with high-contrast coefficients,…
Prediction of Multiscale Features Using Deep Learning-based Preconditioner-Solver Architecture for Darcy Equation in High-Contrast Media
Jie Chen, Peiqi Li, Zhengkang He +1
Modeling subsurface fluid flow in porous media is crucial for applications such as oil and gas exploration. However, the inherent heterogeneity and multi-scale characteristics of t…
Kinetics of Rayleigh-Taylor instability in van der Waals fluid: the influence of compressibility
Jie Chen, Aiguo Xu, Yudong Zhang +2
Early studies on Rayleigh-Taylor instability (RTI) primarily relied on the Navier-Stokes (NS) model. As research progresses, it becomes increasingly evident that the kinetic inform…