3.4k citations · 27.2k across the 213 of their papers we have counts for
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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…
Physics-informed neural networks for solving forward and inverse flow problems via the Boltzmann-BGK formulation
Qin Lou, Xuhui Meng, George Em Karniadakis
In this study, we employ physics-informed neural networks (PINNs) to solve forward and inverse problems via the Boltzmann-BGK formulation (PINN-BGK), enabling PINNs to model flows…
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…
Solving Inverse Stochastic Problems from Discrete Particle Observations Using the Fokker-Planck Equation and Physics-informed Neural Networks
Xiaoli Chen, Liu Yang, Jinqiao Duan +1
The Fokker-Planck (FP) equation governing the evolution of the probability density function (PDF) is applicable to many disciplines but it requires specification of the coefficient…
Non-invasive Inference of Thrombus Material Properties with Physics-informed Neural Networks
Minglang Yin, Xiaoning Zheng, Jay D. Humphrey +1
We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs on inferring the thr…