Fei Lu

Orcid: 0000-0001-6842-7922

Affiliations:
  • Johns Hopkins University, Department of Mathematics, Baltimore, MD, USA
  • University of California Berkeley, CA, USA (former)
  • Lawrence Berkeley National Laboratory, CA, USA (former)


According to our database1, Fei Lu authored at least 24 papers between 2015 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Robust First and Second-Order Differentiation for Regularized Optimal Transport.
CoRR, 2024

Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel.
CoRR, 2024

2023
NySALT: Nyström-type inference-based schemes adaptive to large time-stepping.
J. Comput. Phys., March, 2023

Small noise analysis for Tikhonov and RKHS regularizations.
CoRR, 2023

Benchmarking optimality of time series classification methods in distinguishing diffusions.
CoRR, 2023

2022
Learning Interaction Kernels in Mean-Field Equations of First-Order Systems of Interacting Particles.
SIAM J. Sci. Comput., 2022

Learning Interaction Kernels in Stochastic Systems of Interacting Particles from Multiple Trajectories.
Found. Comput. Math., 2022

A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators.
CoRR, 2022

Stochastic Data-Driven Variational Multiscale Reduced Order Models.
CoRR, 2022

Unsupervised learning of observation functions in state-space models by nonparametric moment methods.
CoRR, 2022

Nonparametric learning of kernels in nonlocal operators.
CoRR, 2022

Data adaptive RKHS Tikhonov regularization for learning kernels in operators.
Proceedings of the Mathematical and Scientific Machine Learning, 2022

2021
Cluster Prediction for Opinion Dynamics From Partial Observations.
IEEE Trans. Signal Inf. Process. over Networks, 2021

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories.
J. Mach. Learn. Res., 2021

Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism.
J. Comput. Phys., 2021

Shock trace prediction by reduced models for a viscous stochastic Burgers equation.
CoRR, 2021

Identifiability of interaction kernels in mean-field equations of interacting particles.
CoRR, 2021

ISALT: Inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems.
CoRR, 2021

2020
Data-Driven Model Reduction for Stochastic Burgers Equations.
Entropy, 2020

On the coercivity condition in the learning of interacting particle systems.
CoRR, 2020

Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles.
CoRR, 2020

2019
Data-driven model reduction, Wiener projections, and the Mori-Zwanzig formalism.
CoRR, 2019

2018
Nonparametric inference of interaction laws in systems of agents from trajectory data.
CoRR, 2018

2015
Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems.
J. Comput. Phys., 2015


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