140 citations · 189 across the 5 of their papers we have counts for
4 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…
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators
Zhiping Mao, Lu Lu, Olaf Marxen +2
In high-speed flow past a normal shock, the fluid temperature rises rapidly triggering downstream chemical dissociation reactions. The chemical changes lead to appreciable changes…
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks
Shengze Cai, Zhicheng Wang, Lu Lu +2
Electroconvection is a multiphysics problem involving coupling of the flow field with the electric field as well as the cation and anion concentration fields. For small Debye lengt…
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Yuyao Chen, Lu Lu, George Em Karniadakis +1
In this paper we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials a…