Qianxiao Li

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Weinan EProfessor of Mathematics, Princeton UniversityE-mailová adresa ověřena na: math.princeton.edu
Tonio BuonassisiMassachusetts Institute of TechnologyE-mailová adresa ověřena na: mit.edu
ZEKUN RENSingapore MIT Alliance for Research and TechnologyE-mailová adresa ověřena na: smart.mit.edu
Kedar HippalgaonkarNanyang Technological University (MSE), A*STAR (IMRE)E-mailová adresa ověřena na: ntu.edu.sg
Siyu Isaac Parker TianPhD, National University of SingaporeE-mailová adresa ověřena na: u.nus.edu
Ioannis KevrekidisProfessor, Chemical Engineering & Applied MathematicsE-mailová adresa ověřena na: jhu.edu
Felix DietrichProfessor for Physics-Enhanced Machine Learning, Technical University of MunichE-mailová adresa ověřena na: tum.de
Cheng TaiMoqi,Inc.E-mailová adresa ověřena na: moqi.ai
Daniil BashNational University of SingaporeE-mailová adresa ověřena na: u.nus.edu
Saif A KhanProfessor, National University of SingaporeE-mailová adresa ověřena na: nus.edu.sg
Zuowei ShenNational University of SingaporeE-mailová adresa ověřena na: nus.edu.sg
Prof Armin ABERLENational University of SingaporeE-mailová adresa ověřena na: nus.edu.sg
Felipe OviedoMITE-mailová adresa ověřena na: mit.edu
Erik BolltClarkson UniversityE-mailová adresa ověřena na: clarkson.edu
Jiequn HanFlatiron Institute, Simons FoundationE-mailová adresa ověřena na: flatironinstitute.org
Yee-Fun LimInstitute of Sustainability for Chemicals, Energy and EnvironmentE-mailová adresa ověřena na: isce2.a-star.edu.sg
Xiaonan Wang (王笑楠)Tsinghua University & National University of SingaporeE-mailová adresa ověřena na: tsinghua.edu.cn
Zhong LiMicrosoft Research AsiaE-mailová adresa ověřena na: microsoft.com
Yongqiang CaiBeijing Normal UniversityE-mailová adresa ověřena na: bnu.edu.cn
Ting LinSchool of Mathematical Sciences, Peking UniversityE-mailová adresa ověřena na: pku.edu.cn

Sledovat

Qianxiao Li

Assistant Professor, Department of Mathematics and Institute for Functional Intelligent Materials

E-mailová adresa ověřena na: nus.edu.sg - Domovská stránka

applied mathematics machine learning scientific computing control theory materials science


Název Seřadit podle citací Seřadit podle roku Seřadit podle názvu	Citace Citace	Rok
Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator Q Li, F Dietrich, EM Bollt, IG Kevrekidis Chaos: An Interdisciplinary Journal of Nonlinear Science 27 (10), 2017	476	2017
Stochastic modified equations and adaptive stochastic gradient algorithms Q Li, C Tai, W E Proceedings of the 34th International Conference on Machine Learning 70 …, 2017	351	2017
Maximum Principle Based Algorithms for Deep Learning Q Li, L Chen, C Tai, W E Journal of Machine Learning Research 18 (165), 1-29, 2018	254	2018
Stochastic modified equations and dynamics of stochastic gradient algorithms i: Mathematical foundations Q Li, C Tai, E Weinan Journal of Machine Learning Research 20 (40), 1-47, 2019	192	2019
An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties Z Ren, SIP Tian, J Noh, F Oviedo, G Xing, J Li, Q Liang, R Zhu, AG Aberle, ... Matter 5 (1), 314-335, 2022	177*	2022
A mean-field optimal control formulation of deep learning E Weinan, J Han, Q Li Research in the Mathematical Sciences 6 (1), 1-41, 2019	163	2019
Two-step machine learning enables optimized nanoparticle synthesis F Mekki-Berrada, Z Ren, T Huang, WK Wong, F Zheng, J Xie, IPS Tian, ... npj Computational Materials 7 (1), 55, 2021	156	2021
Deep learning via dynamical systems: An approximation perspective Q Li, T Lin, Z Shen Journal of the European Mathematical Society 25 (5), 1671-1709, 2023	118	2023
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks Q Li, S Hao Proceedings of the 35th International Conference on Machine Learning 80 …, 2018	97	2018
Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics K Hippalgaonkar, Q Li, X Wang, JW Fisher III, J Kirkpatrick, T Buonassisi Nature Reviews Materials 8 (4), 241-260, 2023	91	2023
Computing committor functions for the study of rare events using deep learning Q Li, B Lin, W Ren The Journal of Chemical Physics 151 (5), 2019	84	2019
Machine learning enables polymer cloud-point engineering via inverse design JN Kumar, Q Li, KYT Tang, T Buonassisi, AL Gonzalez-Oyarce, J Ye npj Computational Materials 5 (1), 73, 2019	62	2019
Challenges and opportunities of polymer design with machine learning and high throughput experimentation JN Kumar, Q Li, Y Jun Mrs Communications 9 (2), 537-544, 2019	62	2019
Noisy Hegselmann-Krause systems: phase transition and the 2r-conjecture C Wang, Q Li, W E, B Chazelle Journal of Statistical Physics 166 (5), 1209-1225, 2017	58	2017
OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle H Yu, X Tian, W E, Q Li Physical Review Fluids 6 (11), 114402, 2021	55	2021
Well-posedness of the limiting equation of a noisy consensus model in opinion dynamics B Chazelle, Q Jiu, Q Li, C Wang Journal of Differential Equations 263 (1), 365-397, 2017	51	2017
Prediction of interstitial diffusion activation energies of nitrogen, oxygen, boron and carbon in bcc, fcc, and hcp metals using machine learning Y Zeng, Q Li, K Bai Computational Materials Science 144, 232-247, 2018	50	2018
Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics Z Ren, F Oviedo, M Thway, SIP Tian, Y Wang, H Xue, JD Perea, ... npj Computational Materials 6 (1), 1-9, 2020	47	2020
A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent Y Cai, Q Li, Z Shen International Conference on Machine Learning, 882-890, 2019	46	2019
Multi‐fidelity high‐throughput optimization of electrical conductivity in P3HT‐CNT composites D Bash, Y Cai, V Chellappan, SL Wong, X Yang, P Kumar, JD Tan, ... Advanced Functional Materials 31 (36), 2102606, 2021	45	2021

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