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Research Focus & Metrics
Explore full metrics on Scilit
Academic Career Length
11Years (2016-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications24 May 2021 in Nature Reviews Physics
Journal Article
George KarniadakisIKIoannis G. Kevrekidis
Ioannis G. Kevrekidis
LLLu Lu
Lu LuPPParis Perdikaris
Paris Perdikaris
SWSifan Wang
Sifan Wang
LYLiu Yang
Liu Yang18 March 2021 in Nature Machine Intelligence
Journal Article
LLLu Lu
Lu LuPJPengzhan Jin
Pengzhan JinGPGuofei Pang
Guofei Pang
ZZZhongqiang Zhang
Zhongqiang ZhangGeorge Karniadakis1 January 2021 in SIAM Review
Journal Article
Deep learning has achieved remarkable success in diverse applications; however, its use in solving partial differential equations (PDEs) has emerged only recently. Here, we present an overview of p...
1 January 2019 in SIAM Journal on Scientific Computing
Journal Article
Physics-informed neural networks (PINNs), introduced in [M. Raissi, P. Perdikaris, and G. Karniadakis, J. Comput. Phys., 378 (2019), pp. 686--707], are effective in solving integer-order partial di...
22 October 2022 in Computer Methods in Applied Mechanics and Engineering
Journal Article
6 April 2020 in Optics Express
Journal Article
YCYuyao Chen
Yuyao Chen
LLLu Lu
Lu LuGKGeorge Em Karniadakis
George Em Karniadakis
LNLuca Dal Negro
Luca Dal Negro
In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.
About Me
Lu Lu is an Assistant Professor in the Department of Statistics and Data Science at Yale University.Prior to joining Yale, he was an Assistant Professor in the Department of Chemical and Biomolecular Engineering at University of Pennsylvania from 2021 to 2023, and an Applied Mathematics Instructor in the Department of Mathematics at Massachusetts Institute of Technology from 2020 to 2021. He obtained his Ph.D. degree in Applied Mathematics at Brown University in 2020, master's degrees in Engineering, Applied Mathematics, and Computer Science at Brown University, and bachelor's degrees in Mechanical Engineering, Economics, and Computer Science at Tsinghua University in 2013. His current research interest lies in scientific machine learning, including theory, algorithms, software, and its applications to engineering, physical, and biological problems. His broad research interests focus on multiscale modeling and high performance computing for physical and biological systems.