2.5k citations · 6.9k across the 23 of their papers we have counts for
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Monte Carlo Physics-informed Neural Networks for Inverse Multiscale Heat Conduction Problems via the Phonon Boltzmann Transport Equation
Qingyi Lin, Chuang Zhang, Xuhui Meng +1
Inferring thermal fields and thermophysical properties from limited measurements is a fundamental challenge in micro- and nanoscale heat conduction, where the classical Fourier law…
Monte Carlo Physics-informed neural networks for multiscale heat conduction via phonon Boltzmann transport equation
Qingyi Lin, Chuang Zhang, Xuhui Meng +1
The phonon Boltzmann transport equation (BTE) is widely used for describing multiscale heat conduction (from nm to m or mm) in solid materials. Developing numerical approaches t…
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
Lu Lu, Xuhui Meng, Shengze Cai +4
Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applica…
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…
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…
Discrete effect on the anti-bounce-back boundary condition of lattice Bhatnagar-Gross-Krook model for convection-diffusion equations
Liang Wang, Xuhui Meng, Hao-Chi Wu +2
The discrete effect on the boundary condition has been a fundamental topic for the lattice Boltzmann method in simulating heat and mass transfer problems. In previous works based o…
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems
Xuhui Meng, George Em Karniadakis
We propose a new composite neural network (NN) that can be trained based on multi-fidelity data. It is comprised of three NNs, with the first NN trained using the low-fidelity data…