140 citations · 150 across the 4 of their papers we have counts for
8 papers · 1 filter
Operator learning for predicting multiscale bubble growth dynamics
Chensen Lin, Zhen Li, Lu Lu +3
Simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investiga…
Active- and transfer-learning applied to microscale-macroscale coupling to simulate viscoelastic flows
Lifei Zhao, Zhen Li, Zhicheng Wang +3
Active- and transfer-learning are applied to polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The resul…
Mesoscopic modeling of heptane: A surface tension calculation
Qi Rao, Yidong Xia, Jiaoyan Li +3
Accurate and efficient flow models for hydrocarbons are important in the development of enhanced geotechnical engineering for energy source recovery and carbon capture & storage in…
PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs
Xuhui Meng, Zhen Li, Dongkun Zhang +1
Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accura…
Supervised parallel-in-time algorithm for long-time Lagrangian simulations of stochastic dynamics: Application to hydrodynamics
Ansel L. Blumers, Zhen Li, George Em Karniadakis
Lagrangian particle methods based on detailed atomic and molecular models are powerful computational tools for studying the dynamics of microscale and nanoscale systems. However, t…
A GPU-accelerated package for simulation of flow in nanoporous source rocks with many-body dissipative particle dynamics
Yidong Xia, Ansel Blumers, Zhen Li +7
Mesoscopic simulations of hydrocarbon flow in source shales are challenging, in part due to the heterogeneous shale pores with sizes ranging from a few nanometers to a few micromet…