| Nonparametric inference of interaction laws in systems of agents from trajectory data F Lu, M Zhong, S Tang, M Maggioni Proceedings of the National Academy of Sciences 116 (29), 14424-14433, 2019 | 116 | 2019 |
| Discrete approach to stochastic parametrization and dimension reduction in nonlinear dynamics AJ Chorin, F Lu Proceedings of the National Academy of Sciences 112 (32), 9804-9809, 2015 | 101 | 2015 |
| Data-based stochastic model reduction for the Kuramoto–Sivashinsky equation F Lu, KK Lin, AJ Chorin Physica D: Nonlinear Phenomena 340, 46-57, 2017 | 82 | 2017 |
| Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism KK Lin, F Lu Journal of Computational Physics, 2020 | 73 | 2020 |
| Learning interaction kernels in heterogeneous systems of agents from multiple trajectories F Lu, M Maggioni, S Tang Journal of Machine Learning Research 22 (32), 1-67, 2021 | 65 | 2021 |
| Limitations of polynomial chaos expansions in the Bayesian solution of inverse problems F Lu, M Morzfeld, X Tu, AJ Chorin Journal of Computational Physics 282, 138-147, 2015 | 54 | 2015 |
| Feynman–Kac formula for the heat equation driven by fractional noise with Hurst parameter H< 1/2 Y Hu, F Lu, D Nualart Ann. Probab. 40 (3), 1041-1068, 2012 | 48 | 2012 |
| On the identifiability of interaction functions in systems of interacting particles Z Li, F Lu, M Maggioni, S Tang, C Zhang Stochastic Processes and Applications 132, 135-163, 2021 | 34 | 2021 |
| Convergence of densities of some functionals of Gaussian processes Y Hu, F Lu, D Nualart Journal of Functional Analysis 266 (2), 814-875, 2014 | 33 | 2014 |
| Comparison of continuous and discrete-time data-based modeling for hypoelliptic systems F Lu, K Lin, A Chorin Communications in Applied Mathematics and Computational Science 11 (2), 187-216, 2016 | 28 | 2016 |
| Learning interaction kernels in mean-field equations of 1st-order systems of interacting particles Q Lang, F Lu SIAM Journal on Scientific Computing 44 (1), A260–A285, 2022 | 23 | 2022 |
| Data-driven model reduction for stochastic Burgers equations F Lu Entropy 22 (12), 2020 | 20 | 2020 |
| Accounting for model error from unresolved scales in ensemble Kalman filters by stochastic parameterization F Lu, X Tu, AJ Chorin Monthly Weather Review 145 (9), 3709-3723, 2017 | 19 | 2017 |
| Identifiability of interaction kernels in mean-field equations of interacting particles Q Lang, F Lu Foundations of Data Science (FoDS) 5 (4), 480-502, 2023 | 15 | 2023 |
| Sampling, feasibility, and priors in Bayesian estimation AJ Chorin, F Lu, RN Miller, M Morzfeld, X Tu Discrete and Continuous Dynamical Systems-Series A 36 (8), 2016 | 15 | 2016 |
| Hölder continuity of the solutions for a class of nonlinear SPDE’s arising from one dimensional superprocesses Y Hu, F Lu, D Nualart Probability Theory and Related Fields 156, 27-49, 2013 | 14 | 2013 |
| Data adaptive RKHS Tikhonov regularization for learning kernels in operators F Lu, Q Lang, Q An Mathematical and Scientific Machine Learning, PMLR, 158-172, 2022 | 13 | 2022 |
| Nonparametric learning of kernels in nonlocal operators F Lu, Q An, Y Yu Journal of Peridynamics and Nonlocal Modeling, 2023 | 10 | 2023 |
| Learning interaction kernels in stochastic systems of interacting particles from multiple trajectories F Lu, M Maggioni, S Tang arXiv preprint arXiv:2007.15174, 2020 | 9 | 2020 |
| Cluster Prediction for Opinion Dynamics from Partial Observations Z Zhang, F Lu IEEE Transactions on Signal and Information Processing over Networks 7, 101 …, 2020 | 6 | 2020 |
Mauro MaggioniBloomberg Distinguished Professor of Mathematics, and Applied Mathematics and Statistics在 jhu.edu 的电子邮件经过验证
