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ORCIDJohn Harlim
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- affiliation: North Carolina State University, Raleigh, USA
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2020 – today
- 2025
[c1]Daning Huang, Hanyang He, John Harlim, Yan Li:
Learning vector fields of differential equations on manifolds with geometrically constrained operator-valued kernels. ICLR 2025- 2024
- [j27]Shixiao Willing Jiang
, Rongji Li, Qile Yan, John Harlim:
Generalized finite difference method on unknown manifolds. J. Comput. Phys. 502: 112812 (2024) - [i17]Yin Yu, John Harlim, Daning Huang, Yan Li:
Learning Coarse-Grained Dynamics on Graph. CoRR abs/2405.09324 (2024) - [i16]John Wilson Peoples, John Harlim:
A Higher Order Local Mesh Method for Approximating Laplacians on Unknown Manifolds. CoRR abs/2405.15735 (2024) - [i15]Anran Jiao, Qile Yan, John Harlim, Lu Lu:
Solving forward and inverse PDE problems on unknown manifolds via physics-informed neural operators. CoRR abs/2407.05477 (2024) - 2023
- [j26]Di Qi, John Harlim:
A data-driven statistical-stochastic surrogate modeling strategy for complex nonlinear non-stationary dynamics. J. Comput. Phys. 485: 112085 (2023) - [j25]Qile Yan, Shixiao Willing Jiang, John Harlim:
Spectral methods for solving elliptic PDEs on unknown manifolds. J. Comput. Phys. 486: 112132 (2023) - [j24]John Harlim, Shixiao Willing Jiang, John Wilson Peoples:
Radial Basis Approximation of Tensor Fields on Manifolds: From Operator Estimation to Manifold Learning. J. Mach. Learn. Res. 24: 345:1-345:85 (2023) - [j23]Qile Yan, Shixiao W. Jiang, John Harlim:
Kernel-Based Methods for Solving Time-Dependent Advection-Diffusion Equations on Manifolds. J. Sci. Comput. 94(1): 5 (2023) - [j22]Yiqi Gu, John Harlim, Senwei Liang, Haizhao Yang:
Stationary Density Estimation of Itô Diffusions Using Deep Learning. SIAM J. Numer. Anal. 61(1): 45-82 (2023) - [i14]Shixiao W. Jiang, Rongji Li, Qile Yan, John Harlim:
Generalized Finite Difference Method on unknown manifolds. CoRR abs/2307.07617 (2023) - 2022
- [i13]John Harlim, Shixiao Willing Jiang, John Wilson Peoples:
Radial basis approximation of tensor fields on manifolds: From operator estimation to manifold learning. CoRR abs/2208.08369 (2022) - [i12]Qile Yan, Shixiao W. Jiang, John Harlim:
Spectral methods for solving elliptic PDEs on unknown manifolds. CoRR abs/2210.10527 (2022) - 2021
- [j21]John Harlim, Shixiao W. Jiang, Senwei Liang, Haizhao Yang:
Machine learning for prediction with missing dynamics. J. Comput. Phys. 428: 109922 (2021) - [j20]He Zhang, John Harlim, Xiantao Li:
Linear response based parameter estimation in the presence of model error. J. Comput. Phys. 430: 110112 (2021) - [i11]He Zhang, John Harlim, Xiantao Li:
Error Bounds of the Invariant Statistics in Machine Learning of Ergodic Itô Diffusions. CoRR abs/2105.10102 (2021) - [i10]Qile Yan, Shixiao Willing Jiang, John Harlim:
Kernel-based methods for Solving Time-Dependent Advection-Diffusion Equations on Manifolds. CoRR abs/2105.13835 (2021) - [i9]Senwei Liang, Shixiao W. Jiang, John Harlim, Haizhao Yang:
Solving PDEs on Unknown Manifolds with Machine Learning. CoRR abs/2106.06682 (2021) - [i8]John Harlim, Shixiao W. Jiang, Hwanwoo Kim, Daniel Sanz-Alonso:
Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries. CoRR abs/2106.06787 (2021) - [i7]Yiqi Gu, John Harlim, Senwei Liang, Haizhao Yang:
Stationary Density Estimation of Itô Diffusions Using Deep Learning. CoRR abs/2109.03992 (2021) - [i6]J. Wilson Peoples, John Harlim:
Spectral Convergence of Symmetrized Graph Laplacian on manifolds with boundary. CoRR abs/2110.06988 (2021) - 2020
- [j19]John Harlim, Daniel Sanz-Alonso, Ruiyi Yang:
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. SIAM/ASA J. Uncertain. Quantification 8(4): 1414-1445 (2020) - [i5]Shixiao W. Jiang, John Harlim:
Ghost Point Diffusion Maps for solving elliptic PDE's on Manifolds with Classical Boundary Conditions. CoRR abs/2006.04002 (2020)
2010 – 2019
- 2019
- [j18]Shixiao W. Jiang, John Harlim:
Parameter Estimation with Data-Driven Nonparametric Likelihood Functions. Entropy 21(6): 559 (2019) - [j17]Faheem Gilani, John Harlim:
Approximating solutions of linear elliptic PDE's on a smooth manifold using local kernel. J. Comput. Phys. 395: 563-582 (2019) - [i4]John Harlim, Shixiao W. Jiang, Senwei Liang, Haizhao Yang:
Machine Learning for Prediction with Missing Dynamics. CoRR abs/1910.05861 (2019) - [i3]John Harlim, Daniel Sanz-Alonso, Ruiyi Yang:
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds. CoRR abs/1910.10669 (2019) - [i2]He Zhang, John Harlim, Xiantao Li:
Linear Response Based Parameter Estimation in the Presence of Model Error. CoRR abs/1910.14113 (2019) - [i1]He Zhang, John Harlim, Xiantao Li:
Kernel Embedding Linear Response. CoRR abs/1912.11110 (2019) - 2018
- [j16]John Harlim, Haizhao Yang:
Diffusion Forecasting Model with Basis Functions from QR-Decomposition. J. Nonlinear Sci. 28(3): 847-872 (2018) - 2016
- [j15]Tyrus Berry, John Harlim:
Semiparametric modeling: Correcting low-dimensional model error in parametric models. J. Comput. Phys. 308: 305-321 (2016) - 2015
- [j14]John Harlim, Hoon Hong, Jacob L. Robbins:
An algebraic method for constructing stable and consistent autoregressive filters. J. Comput. Phys. 283: 241-257 (2015) - [j13]Yicun Zhen, John Harlim:
Adaptive error covariances estimation methods for ensemble Kalman filters. J. Comput. Phys. 294: 619-638 (2015) - [j12]Tyrus Berry, John Harlim:
Nonparametric Uncertainty Quantification for Stochastic Gradient Flows. SIAM/ASA J. Uncertain. Quantification 3(1): 484-508 (2015) - 2014
- [j11]John Harlim, Adam Mahdi, Andrew J. Majda:
An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models. J. Comput. Phys. 257: 782-812 (2014) - 2013
- [j10]Kristen A. Brown, John Harlim:
Assimilating irregularly spaced sparsely observed turbulent signals with hierarchical Bayesian reduced stochastic filters. J. Comput. Phys. 235: 143-160 (2013) - [j9]Eugenia S. Bakunova, John Harlim:
Optimal filtering of complex turbulent systems with memory depth through consistency constraints. J. Comput. Phys. 237: 320-343 (2013) - [j8]John Harlim, Andrew J. Majda:
Test Models for Filtering with Superparameterization. Multiscale Model. Simul. 11(1): 282-308 (2013) - 2011
- [j7]John Harlim:
Numerical strategies for filtering partially observed stiff stochastic differential equations. J. Comput. Phys. 230(3): 744-762 (2011) - [j6]John Harlim:
Interpolating Irregularly Spaced Observations for Filtering Turbulent Complex Systems. SIAM J. Sci. Comput. 33(5): 2620-2640 (2011) - 2010
- [j5]Boris Gershgorin, John Harlim, Andrew J. Majda:
Test models for improving filtering with model errors through stochastic parameter estimation. J. Comput. Phys. 229(1): 1-31 (2010) - [j4]Boris Gershgorin, John Harlim, Andrew J. Majda:
Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation. J. Comput. Phys. 229(1): 32-57 (2010)
2000 – 2009
- 2008
- [j3]Emilio Castronovo, John Harlim, Andrew J. Majda:
Mathematical test criteria for filtering complex systems: Plentiful observations. J. Comput. Phys. 227(7): 3678-3714 (2008) - [j2]John Harlim, Andrew J. Majda:
Mathematical strategies for filtering complex systems: Regularly spaced sparse observations. J. Comput. Phys. 227(10): 5304-5341 (2008) - 2007
- [j1]John Harlim, William F. Langford:
The Cusp-hopf bifurcation. Int. J. Bifurc. Chaos 17(8): 2547-2570 (2007)
Coauthor Index
aka: Shixiao Willing Jiang
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