John Harlim

Affiliations:
  • North Carolina State University, Raleigh, USA


According to our database1, John Harlim authored at least 33 papers between 2007 and 2023.

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

Timeline

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Online presence:

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Bibliography

2023
Stationary Density Estimation of Itô Diffusions Using Deep Learning.
SIAM J. Numer. Anal., February, 2023

Kernel-Based Methods for Solving Time-Dependent Advection-Diffusion Equations on Manifolds.
J. Sci. Comput., 2023

Spectral methods for solving elliptic PDEs on unknown manifolds.
J. Comput. Phys., 2023

A data-driven statistical-stochastic surrogate modeling strategy for complex nonlinear non-stationary dynamics.
J. Comput. Phys., 2023

Generalized Finite Difference Method on unknown manifolds.
CoRR, 2023

2022
Radial basis approximation of tensor fields on manifolds: From operator estimation to manifold learning.
CoRR, 2022

2021
Linear response based parameter estimation in the presence of model error.
J. Comput. Phys., 2021

Machine learning for prediction with missing dynamics.
J. Comput. Phys., 2021

Spectral Convergence of Symmetrized Graph Laplacian on manifolds with boundary.
CoRR, 2021

Graph-based Prior and Forward Models for Inverse Problems on Manifolds with Boundaries.
CoRR, 2021

Solving PDEs on Unknown Manifolds with Machine Learning.
CoRR, 2021

Error Bounds of the Invariant Statistics in Machine Learning of Ergodic Itô Diffusions.
CoRR, 2021

2020
Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds.
SIAM/ASA J. Uncertain. Quantification, 2020

Ghost Point Diffusion Maps for solving elliptic PDE's on Manifolds with Classical Boundary Conditions.
CoRR, 2020

2019
Approximating solutions of linear elliptic PDE's on a smooth manifold using local kernel.
J. Comput. Phys., 2019

Parameter Estimation with Data-Driven Nonparametric Likelihood Functions.
Entropy, 2019

Kernel Embedding Linear Response.
CoRR, 2019

2018
Diffusion Forecasting Model with Basis Functions from QR-Decomposition.
J. Nonlinear Sci., 2018

2016
Semiparametric modeling: Correcting low-dimensional model error in parametric models.
J. Comput. Phys., 2016

2015
Nonparametric Uncertainty Quantification for Stochastic Gradient Flows.
SIAM/ASA J. Uncertain. Quantification, 2015

Adaptive error covariances estimation methods for ensemble Kalman filters.
J. Comput. Phys., 2015

An algebraic method for constructing stable and consistent autoregressive filters.
J. Comput. Phys., 2015

2014
An ensemble Kalman filter for statistical estimation of physics constrained nonlinear regression models.
J. Comput. Phys., 2014

2013
Test Models for Filtering with Superparameterization.
Multiscale Model. Simul., 2013

Assimilating irregularly spaced sparsely observed turbulent signals with hierarchical Bayesian reduced stochastic filters.
J. Comput. Phys., 2013

Optimal filtering of complex turbulent systems with memory depth through consistency constraints.
J. Comput. Phys., 2013

2011
Interpolating Irregularly Spaced Observations for Filtering Turbulent Complex Systems.
SIAM J. Sci. Comput., 2011

Numerical strategies for filtering partially observed stiff stochastic differential equations.
J. Comput. Phys., 2011

2010
Improving filtering and prediction of spatially extended turbulent systems with model errors through stochastic parameter estimation.
J. Comput. Phys., 2010

Test models for improving filtering with model errors through stochastic parameter estimation.
J. Comput. Phys., 2010

2008
Mathematical strategies for filtering complex systems: Regularly spaced sparse observations.
J. Comput. Phys., 2008

Mathematical test criteria for filtering complex systems: Plentiful observations.
J. Comput. Phys., 2008

2007
The Cusp-hopf bifurcation.
Int. J. Bifurc. Chaos, 2007


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