18 citations · 37 across the 4 of their papers we have counts for
5 papers · 1 filter
Variational learning of energy-stable macroscopic flow equations from microscopic laws
Pei Ge, Huan Lei
We present a generalized variational approach for constructing energy-stable macroscopic partial differential equations (PDEs) directly from microscopic physical laws. Rather than…
Data-driven learning of the generalized Langevin equation with state-dependent memory
Pei Ge, Zhongqiang Zhang, Huan Lei
We present a data-driven method to learn stochastic reduced models of complex systems that retain a state-dependent memory beyond the standard generalized Langevin equation (GLE) w…
Machine learning assisted coarse-grained molecular dynamics modeling of meso-scale interfacial fluids
Pei Ge, Linfeng Zhang, Huan Lei
A hallmark of meso-scale interfacial fluids is the multi-faceted, scale-dependent interfacial energy, which often manifests different characteristics across the molecular and conti…
Data-driven construction of stochastic reduced dynamics encoded with non-Markovian features
Zhiyuan She, Pei Ge, Huan Lei
One important problem in constructing the reduced dynamics of molecular systems is the accurate modeling of the non-Markovian behavior arising from the dynamics of unresolved varia…
DeePN: A deep learning-based non-Newtonian hydrodynamic model
Lidong Fang, Pei Ge, Lei Zhang +2
A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully enco…