Qianxiao Li
Qianxiao Li
Assistant Professor, Department of Mathematics and Institute for Functional Intelligent Materials
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
Deep learning via dynamical systems: An approximation perspective
Q Li, T Lin, Z Shen
Journal of the European Mathematical Society 25 (5), 1671-1709, 2022
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
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
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
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
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
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
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
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
On Matching, and Even Rectifying, Dynamical Systems through Koopman Operator Eigenfunctions
EM Bollt, Q Li, F Dietrich, I Kevrekidis
SIAM Journal on Applied Dynamical Systems 17 (2), 1925-1960, 2018
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
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
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, J Dario Perea, ...
npj Computational Materials 6 (1), 9, 2020
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