Fei Lu

According to our database, Fei Lu authored at least 24 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Robust First and Second-Order Differentiation for Regularized Optimal Transport.

Xingjie Li, Fei Lu, Molei Tao, Felix X.-F. Ye

CoRR, 2024

Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel.

Quanjun Lang, Xiong Wang, Fei Lu, Mauro Maggioni

CoRR, 2024

2023

NySALT: Nyström-type inference-based schemes adaptive to large time-stepping.

Xingjie Li, Fei Lu, Molei Tao, Felix X.-F. Ye

J. Comput. Phys., March, 2023

Small noise analysis for Tikhonov and RKHS regularizations.

Quanjun Lang, Fei Lu

CoRR, 2023

Benchmarking optimality of time series classification methods in distinguishing diffusions.

Zehong Zhang, Fei Lu, Esther Xu Fei, Terry Lyons, Yannis G. Kevrekidis, Tom Woolf

CoRR, 2023

2022

Learning Interaction Kernels in Mean-Field Equations of First-Order Systems of Interacting Particles.

Quanjun Lang, Fei Lu

SIAM J. Sci. Comput., 2022

Learning Interaction Kernels in Stochastic Systems of Interacting Particles from Multiple Trajectories.

Fei Lu, Mauro Maggioni, Sui Tang

Found. Comput. Math., 2022

A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators.

Neil K. Chada, Quanjun Lang, Fei Lu, Xiong Wang

CoRR, 2022

Stochastic Data-Driven Variational Multiscale Reduced Order Models.

Fei Lu, Changhong Mou, Honghu Liu, Traian Iliescu

CoRR, 2022

Unsupervised learning of observation functions in state-space models by nonparametric moment methods.

Qingci An, Yannis G. Kevrekidis, Fei Lu, Mauro Maggioni

CoRR, 2022

Nonparametric learning of kernels in nonlocal operators.

Fei Lu, Qingci An, Yue Yu

CoRR, 2022

Data adaptive RKHS Tikhonov regularization for learning kernels in operators.

Fei Lu, Quanjun Lang, Qingci An

Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021

Cluster Prediction for Opinion Dynamics From Partial Observations.

Zehong Zhang, Fei Lu

IEEE Trans. Signal Inf. Process. over Networks, 2021

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories.

Fei Lu, Mauro Maggioni, Sui Tang

J. Mach. Learn. Res., 2021

Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism.

Kevin K. Lin, Fei Lu

J. Comput. Phys., 2021

Shock trace prediction by reduced models for a viscous stochastic Burgers equation.

Nan Chen, Honghu Liu, Fei Lu

CoRR, 2021

Identifiability of interaction kernels in mean-field equations of interacting particles.

Quanjun Lang, Fei Lu

CoRR, 2021

ISALT: Inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems.

Xingjie Li, Fei Lu, Felix X.-F. Ye

CoRR, 2021

2020

Data-Driven Model Reduction for Stochastic Burgers Equations.

Fei Lu

Entropy, 2020

On the coercivity condition in the learning of interacting particle systems.

Zhongyang Li, Fei Lu

CoRR, 2020

Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles.

Quanjun Lang, Fei Lu

CoRR, 2020

2019

Data-driven model reduction, Wiener projections, and the Mori-Zwanzig formalism.

Kevin K. Lin, Fei Lu

CoRR, 2019

2018

Nonparametric inference of interaction laws in systems of agents from trajectory data.

Fei Lu, Mauro Maggioni, Sui Tang, Ming Zhong

CoRR, 2018

2015

Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems.

Fei Lu, Matthias Morzfeld, Xuemin Tu, Alexandre J. Chorin

J. Comput. Phys., 2015