Lu Lu
Orcid: 0000-0002-5476-5768Affiliations:
- Brown University, Division of Applied Mathematics, Providence, RI, USA
According to our database1,
Lu Lu
authored at least 26 papers
between 2017 and 2024.
Collaborative distances:
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Bibliography
2024
Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks.
CoRR, 2024
2023
Neural operator prediction of linear instability waves in high-speed boundary layers.
J. Comput. Phys., February, 2023
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration.
CoRR, 2023
2022
SIAM J. Sci. Comput., 2022
Approximation rates of DeepONets for learning operators arising from advection-diffusion equations.
Neural Networks, 2022
Reliable extrapolation of deep neural operators informed by physics or sparse observations.
CoRR, 2022
Effective Data Sampling Strategies and Boundary Condition Constraints of Physics-Informed Neural Networks for Identifying Material Properties in Solid Mechanics.
CoRR, 2022
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks.
CoRR, 2022
2021
SIAM J. Sci. Comput., 2021
How the spleen reshapes and retains young and old red blood cells: A computational investigation.
PLoS Comput. Biol., 2021
Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients.
npj Digit. Medicine, 2021
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators.
Nat. Mach. Intell., 2021
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators.
J. Comput. Phys., 2021
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks.
J. Comput. Phys., 2021
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.
CoRR, 2021
Convergence rate of DeepONets for learning operators arising from advection-diffusion equations.
CoRR, 2021
2020
PLoS Comput. Biol., 2020
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness.
Neural Networks, 2020
2019
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems.
J. Comput. Phys., 2019
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.
CoRR, 2019
2018
2017