Lu Lu

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 31

Top Topics

Numerical Examples

Sampling Strategies

Top Venues

J. Comput. Phys.

PLoS Comput. Biol.

SIAM J. Sci. Comput.

Publications

Anran Jiao, Qile Yan, John Harlim, Lu Lu
Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators. CoRR (2024)
Zhongyi Jiang, Min Zhu, Lu Lu
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration. Reliab. Eng. Syst. Saf. 251 (2024)
Wensi Wu, Mitchell Daneker, Kevin T. Turner, Matthew A. Jolley, Lu Lu
Identifying heterogeneous micromechanical properties of biological tissues via physics-informed neural networks. CoRR (2024)
Zhongyi Jiang, Min Zhu, Dongzhuo Li, Qiuzi Li, Yanhua O. Yuan, Lu Lu
Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration. CoRR (2023)
Benjamin Fan, Edward Qiao, Anran Jiao, Zhouzhou Gu, Wenhao Li, Lu Lu
Deep Learning for Solving and Estimating Dynamic Macro-Finance Models. CoRR (2023)
Patricio Clark Di Leoni, Lu Lu, Charles Meneveau, George Em Karniadakis, Tamer A. Zaki
Neural operator prediction of linear instability waves in high-speed boundary layers. J. Comput. Phys. 474 (2023)
Pengzhan Jin, Shuai Meng, Lu Lu
MIONet: Learning multiple-input operators via tensor product. CoRR (2022)
Mitchell Daneker, Zhen Zhang, George Em Karniadakis, Lu Lu
Systems Biology: Identifiability analysis and parameter identification via systems-biology informed neural networks. CoRR (2022)
Pengzhan Jin, Shuai Meng, Lu Lu
MIONet: Learning Multiple-Input Operators via Tensor Product. SIAM J. Sci. Comput. 44 (6) (2022)
Wensi Wu, Mitchell Daneker, Matthew A. Jolley, Kevin T. Turner, Lu Lu
Effective Data Sampling Strategies and Boundary Condition Constraints of Physics-Informed Neural Networks for Identifying Material Properties in Solid Mechanics. CoRR (2022)
Min Zhu, Handi Zhang, Anran Jiao, George Em Karniadakis, Lu Lu
Reliable extrapolation of deep neural operators informed by physics or sparse observations. CoRR (2022)
Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis
Approximation rates of DeepONets for learning operators arising from advection-diffusion equations. Neural Networks 153 (2022)
Jeremy Yu, Lu Lu, Xuhui Meng, George Em Karniadakis
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems. CoRR (2021)
Shengze Cai, Zhicheng Wang, Lu Lu, Tamer A. Zaki, George Em Karniadakis
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks. J. Comput. Phys. 436 (2021)
Lu Lu, Pengzhan Jin, Guofei Pang, Zhongqiang Zhang, George Em Karniadakis
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat. Mach. Intell. 3 (3) (2021)
Beichuan Deng, Yeonjong Shin, Lu Lu, Zhongqiang Zhang, George Em Karniadakis
Convergence rate of DeepONets for learning operators arising from advection-diffusion equations. CoRR (2021)
He Li, Zixiang Leonardo Liu, Lu Lu, Pierre Buffet, George Em Karniadakis
How the spleen reshapes and retains young and old red blood cells: A computational investigation. PLoS Comput. Biol. 17 (11) (2021)
Yixiang Deng, Lu Lu, Laura Aponte, Angeliki M. Angelidi, Vera Novak, George Em Karniadakis, Christos S. Mantzoros
Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. npj Digit. Medicine 4 (2021)
Zhiping Mao, Lu Lu, Olaf Marxen, Tamer A. Zaki, George Em Karniadakis
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. 447 (2021)
Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
DeepXDE: A Deep Learning Library for Solving Differential Equations. SIAM Rev. 63 (1) (2021)
Alireza Yazdani, Lu Lu, Maziar Raissi, George Em Karniadakis
Systems biology informed deep learning for inferring parameters and hidden dynamics. PLoS Comput. Biol. 16 (11) (2020)
Pengzhan Jin, Lu Lu, Yifa Tang, George Em Karniadakis
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. Neural Networks 130 (2020)
Lu Lu, Xuhui Meng, Zhiping Mao, George Em Karniadakis
DeepXDE: A Deep Learning Library for Solving Differential Equations. AAAI Spring Symposium: MLPS (2020)
Lu Lu, Xuhui Meng, Zhiping Mao, George E. Karniadakis
DeepXDE: A deep learning library for solving differential equations. CoRR (2019)
Lu Lu, Pengzhan Jin, George Em Karniadakis
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. CoRR (2019)
Lu Lu, Yeonjong Shin, Yanhui Su, George E. Karniadakis
Dying ReLU and Initialization: Theory and Numerical Examples. CoRR (2019)
Guofei Pang, Lu Lu, George Em Karniadakis
fPINNs: Fractional Physics-Informed Neural Networks. SIAM J. Sci. Comput. 41 (4) (2019)
Pengzhan Jin, Lu Lu, Yifa Tang, George E. Karniadakis
Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. CoRR (2019)
Dongkun Zhang, Lu Lu, Ling Guo, George Em Karniadakis
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems. J. Comput. Phys. 397 (2019)
Lu Lu, Yanhui Su, George E. Karniadakis
Collapse of Deep and Narrow Neural Nets. CoRR (2018)
Yu-Hang Tang, Lu Lu, He Li, Constantinos Evangelinos, Leopold Grinberg, Vipin Sachdeva, George E. Karniadakis
OpenRBC: A Fast Simulator of Red Blood Cells at Protein Resolution. CoRR (2017)