Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems. - Fei Lu 016; Matthias Morzfeld; Xuemin Tu; Alexandre J. Chorin (J. Comput. Phys., 2015)

Nonparametric inference of interaction laws in systems of agents from trajectory data. - Fei Lu 016; Mauro Maggioni; Sui Tang; Ming Zhong 008 (CoRR, 2018)

Data-driven model reduction, Wiener projections, and the Mori-Zwanzig formalism. - Kevin K. Lin; Fei Lu 016 (CoRR, 2019)

Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles. - Quanjun Lang; Fei Lu 016 (CoRR, 2020)

On the coercivity condition in the learning of interacting particle systems. - Zhongyang Li; Fei Lu 016 (CoRR, 2020)

Data-Driven Model Reduction for Stochastic Burgers Equations. - Fei Lu 016 (Entropy, 2020)

ISALT: Inference-based schemes adaptive to large time-stepping for locally Lipschitz ergodic systems. - Xingjie Li 001; Fei Lu 016; Felix X.-F. Ye (CoRR, 2021)

Identifiability of interaction kernels in mean-field equations of interacting particles. - Quanjun Lang; Fei Lu 016 (CoRR, 2021)

Shock trace prediction by reduced models for a viscous stochastic Burgers equation. - Nan Chen; Honghu Liu; Fei Lu 016 (CoRR, 2021)

Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism. - Kevin K. Lin; Fei Lu 016 (J. Comput. Phys., 2021)

Learning interaction kernels in heterogeneous systems of agents from multiple trajectories. - Fei Lu 016; Mauro Maggioni; Sui Tang (J. Mach. Learn. Res., 2021)

Cluster Prediction for Opinion Dynamics From Partial Observations. - Zehong Zhang; Fei Lu 016 (IEEE Trans. Signal Inf. Process. over Networks, 2021)

Data adaptive RKHS Tikhonov regularization for learning kernels in operators. - Fei Lu 016; Quanjun Lang; Qingci An (MSML, 2022)

Nonparametric learning of kernels in nonlocal operators. - Fei Lu 016; Qingci An; Yue Yu (CoRR, 2022)

Unsupervised learning of observation functions in state-space models by nonparametric moment methods. - Qingci An; Yannis G. Kevrekidis; Fei Lu 016; Mauro Maggioni (CoRR, 2022)

Stochastic Data-Driven Variational Multiscale Reduced Order Models. - Fei Lu 016; Changhong Mou; Honghu Liu; Traian Iliescu (CoRR, 2022)

A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators. - Neil K. Chada; Quanjun Lang; Fei Lu 016; Xiong Wang (CoRR, 2022)

Learning Interaction Kernels in Stochastic Systems of Interacting Particles from Multiple Trajectories. - Fei Lu 016; Mauro Maggioni; Sui Tang (Found. Comput. Math., 2022)

Learning Interaction Kernels in Mean-Field Equations of First-Order Systems of Interacting Particles. - Quanjun Lang; Fei Lu 016 (SIAM J. Sci. Comput., 2022)

Benchmarking optimality of time series classification methods in distinguishing diffusions. - Zehong Zhang; Fei Lu 016; Esther Xu Fei; Terry Lyons; Yannis G. Kevrekidis; Tom Woolf (CoRR, 2023)

Small noise analysis for Tikhonov and RKHS regularizations. - Quanjun Lang; Fei Lu 016 (CoRR, 2023)

NySALT: Nyström-type inference-based schemes adaptive to large time-stepping. - Xingjie Li 001; Fei Lu 016; Molei Tao; Felix X.-F. Ye (J. Comput. Phys., 2023)

Interacting Particle Systems on Networks: joint inference of the network and the interaction kernel. - Quanjun Lang; Xiong Wang; Fei Lu 016; Mauro Maggioni (CoRR, 2024)

Robust First and Second-Order Differentiation for Regularized Optimal Transport. - Xingjie Li 001; Fei Lu 016; Molei Tao; Felix X.-F. Ye (CoRR, 2024)