| Parameter recovery for the 2 dimensional Navier--Stokes equations via continuous data assimilation E Carlson, J Hudson, A Larios SIAM Journal on Scientific Computing 42 (1), A250-A270, 2020 | 63 | 2020 |
| Continuous data assimilation for the 2D magnetohydrodynamic equations using one component of the velocity and magnetic fields A Biswas, J Hudson, A Larios, Y Pei Asymptotic Analysis 108 (1-2), 1-43, 2018 | 39 | 2018 |
| Dynamically learning the parameters of a chaotic system using partial observations E Carlson, J Hudson, A Larios, VR Martinez, E Ng, JP Whitehead arXiv preprint arXiv:2108.08354, 2021 | 36 | 2021 |
| Numerical efficacy study of data assimilation for the 2D magnetohydrodynamic equations J Hudson, M Jolly Journal of computational dynamics 6 (1), 2019 | 21 | 2019 |
| Determining the viscosity of the Navier–Stokes equations from observations of finitely many modes A Biswas, J Hudson Inverse Problems 39 (12), 125012, 2023 | 6 | 2023 |
| Space and time analyticity for inviscid equations of fluid dynamics A Biswas, J Hudson arXiv preprint arXiv:1712.02720, 2017 | 5 | 2017 |
| The role of stiffness in training and generalization of ResNets J Hudson, M D'Elia, HN Najm, K Sargsyan Journal of Machine Learning for Modeling and Computing 4 (2), 2023 | 3 | 2023 |
| Persistence time of solutions of the three-dimensional Navier-Stokes equations in Sobolev-Gevrey classes A Biswas, J Hudson, J Tian Journal of Differential Equations 277, 191-233, 2021 | 2 | 2021 |
| Parameter recovery and sensitivity analysis for the 2d navier-stokes equations via continuous data assimilation E Carlson, J Hudson, A Larios arXiv preprint arXiv:1812.07646, 2018 | 1 | 2018 |
| Measuring Stiffness in Residual Neural Networks J Hudson, M D’Elia, HN Najm, K Sargsyan Reduction, Approximation, Machine Learning, Surrogates, Emulators and …, 2024 | | 2024 |
| Analysis of Neural Networks as Random Dynamical Systems K Sargsyan, JL Hudson, OH Diaz-Ibarra, H Rosso, L Ruthotto, M D'Elia, ... Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2023 | | 2023 |
| Examining stiffness in ResNets through interpretation as discretized Neural ODEs. J Hudson, K Sargsyan, M D'Elia, H Najm Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2022 | | 2022 |
| Detecting stiffness in ResNets inspired by Neural ODEs. J Hudson, K Sargsyan, M D'Elia, H Najm Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| Analysis of Neural Networks as Dynamical Systems. J Hudson, K Sargsyan, M D'Elia, H Najm Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021 | | 2021 |
| AOT Data Assimilation Algorithm: Parameter Recovery, Applications, and a Nonlinear Algorithm EA Carlson, A Larios, J Hudson, L Van Roekel, MR Petersen, ... Los Alamos National Laboratory (LANL), Los Alamos, NM (United States), 2020 | | 2020 |
| Higher Order Regularity, Long Term Dynamics, and Data Assimilation for Magnetohydrodynamic Flows J Hudson University of Maryland, Baltimore County, 2018 | | 2018 |
| Uniquely recovering viscosity in 2D NSE from finitely many determining modes J Hudson, A Biswas, A Larios, E Carlson 2024 Fall Southeastern Sectional Meeting-CANCELLED, 0 | | |
| CANCELLED: The non-overlap of attractors for 2D periodic flows, and when viscosity can be recovered from sparse data. J Hudson 2022 Fall Western Sectional Meeting, 0 | | |
Adam LariosUniversity of Nebraska-LincolnEmail được xác minh tại unl.edu