Artigos com autorizações de acesso público - Lu LuSaiba mais
1 artigo não disponível publicamente
Understanding the Twisted Structure of Amyloid Fibrils via Molecular Simulations
L Lu, Y Deng, X Li, H Li, GE Karniadakis
The Journal of Physical Chemistry B 122 (49), 11302-11310, 2018
Autorizações: US Department of Energy, US National Institutes of Health
39 artigos disponíveis publicamente
Physics-informed machine learning
GE Karniadakis, IG Kevrekidis, L Lu, P Perdikaris, S Wang, L Yang
Nature Reviews Physics 3 (6), 422-440, 2021
Autorizações: US Department of Energy, US Department of Defense
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
L Lu, P Jin, G Pang, Z Zhang, GE Karniadakis
Nature Machine Intelligence 3 (3), 218-229, 2021
Autorizações: US Department of Energy, US Department of Defense
DeepXDE: A deep learning library for solving differential equations
L Lu, X Meng, Z Mao, GE Karniadakis
SIAM Review 63 (1), 208-228, 2021
Autorizações: US Department of Energy, US Department of Defense
fPINNs: Fractional physics-informed neural networks
G Pang, L Lu, GE Karniadakis
SIAM Journal on Scientific Computing 41 (4), A2603-A2626, 2019
Autorizações: US Department of Energy, US Department of Defense
Physics-informed neural networks with hard constraints for inverse design
L Lu, R Pestourie, W Yao, Z Wang, F Verdugo, SG Johnson
SIAM Journal on Scientific Computing 43 (6), B1105-B1132, 2021
Autorizações: Government of Spain
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Y Chen, L Lu, GE Karniadakis, L Dal Negro
Optics Express 28 (8), 11618-11633, 2020
Autorizações: US Department of Energy, US Department of Defense
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
D Zhang, L Lu, L Guo, GE Karniadakis
Journal of Computational Physics 397, 108850, 2019
Autorizações: US Department of Defense, National Natural Science Foundation of China
Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems
J Yu, L Lu, X Meng, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 393, 114823, 2022
Autorizações: US Department of Energy, US Department of Defense
A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data
L Lu, X Meng, S Cai, Z Mao, S Goswami, Z Zhang, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 393, 114778, 2022
Autorizações: US Department of Energy, US Department of Defense
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
C Wu, M Zhu, Q Tan, Y Kartha, L Lu
Computer Methods in Applied Mechanics and Engineering 403, 115671, 2023
Autorizações: US Department of Energy
Extraction of mechanical properties of materials through deep learning from instrumented indentation
L Lu, M Dao, P Kumar, U Ramamurty, GE Karniadakis, S Suresh
Proceedings of the National Academy of Sciences 117 (13), 7052-7062, 2020
Autorizações: US Department of Energy, US Department of Defense
Systems biology informed deep learning for inferring parameters and hidden dynamics
A Yazdani, L Lu, M Raissi, GE Karniadakis
PLOS Computational Biology 16 (11), e1007575, 2020
Autorizações: US National Institutes of Health
DeepM&Mnet: Inferring the electroconvection multiphysics fields based on operator approximation by neural networks
S Cai, Z Wang, L Lu, TA Zaki, GE Karniadakis
Journal of Computational Physics 436, 110296, 2021
Autorizações: US Department of Energy, US Department of Defense
Operator learning for predicting multiscale bubble growth dynamics
C Lin, Z Li, L Lu, S Cai, M Maxey, GE Karniadakis
The Journal of Chemical Physics 154 (10), 104118, 2021
Autorizações: US Department of Energy
DeepM&Mnet for hypersonics: Predicting the coupled flow and finite-rate chemistry behind a normal shock using neural-network approximation of operators
Z Mao, L Lu, O Marxen, TA Zaki, GE Karniadakis
Journal of Computational Physics 447, 110698, 2021
Autorizações: US Department of Defense
MIONet: Learning Multiple-Input Operators via Tensor Product
P Jin, S Meng, L Lu
SIAM Journal on Scientific Computing 44 (6), A3490-A3514, 2022
Autorizações: US Department of Energy, National Natural Science Foundation of China
Mechanics of diseased red blood cells in human spleen and consequences for hereditary blood disorders
H Li, L Lu, X Li, PA Buffet, M Dao, GE Karniadakis, S Suresh
Proceedings of the National Academy of Sciences 115 (38), 9574-9579, 2018
Autorizações: US Department of Energy, US National Institutes of Health
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
L Lu, R Pestourie, SG Johnson, G Romano
Physical Review Research 4 (2), 023210, 2022
Autorizações: US Department of Defense
Neural operator prediction of linear instability waves in high-speed boundary layers
PC Di Leoni, L Lu, C Meneveau, GE Karniadakis, TA Zaki
Journal of Computational Physics, 111793, 2022
Autorizações: US Department of Energy, US Department of Defense
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