L
Lu Lu
Researcher at Massachusetts Institute of Technology
Publications - 56
Citations - 5802
Lu Lu is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 19, co-authored 49 publications receiving 1640 citations. Previous affiliations of Lu Lu include University of Pennsylvania & Brown University.
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Physics-informed machine learning
TL;DR: Some of the prevailing trends in embedding physics into machine learning are reviewed, some of the current capabilities and limitations are presented and diverse applications of physics-informed learning both for forward and inverse problems, including discovering hidden physics and tackling high-dimensional problems are discussed.
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DeepXDE: A deep learning library for solving differential equations
TL;DR: Deep learning has achieved remarkable success in diverse applications; however, its use in solving partial differential equations (PDEs) has emerged only recently as discussed by the authors, and a comprehensive overview of deep learning for PDEs can be found in Section 2.1.
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Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
TL;DR: A new deep neural network called DeepONet can lean various mathematical operators with small generalization error and can learn various explicit operators, such as integrals and fractional Laplacians, as well as implicit operators that represent deterministic and stochastic differential equations.
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DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators
TL;DR: This work proposes deep operator networks (DeepONets) to learn operators accurately and efficiently from a relatively small dataset, and demonstrates that DeepONet significantly reduces the generalization error compared to the fully-connected networks.
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Physics-informed neural networks for inverse problems in nano-optics and metamaterials.
TL;DR: In this paper, a physics-informed neural network (PINN) was applied to retrieve the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles.