| An ensemble-proper orthogonal decomposition method for the nonstationary Navier--Stokes equations M Gunzburger, N Jiang, M Schneier SIAM Journal on Numerical Analysis 55 (1), 286-304, 2017 | 82 | 2017 |
| Continuous data assimilation reduced order models of fluid flow C Zerfas, LG Rebholz, M Schneier, T Iliescu Computer Methods in Applied Mechanics and Engineering 357, 112596, 2019 | 47 | 2019 |
| An artificial compression reduced order model V DeCaria, T Iliescu, W Layton, M McLaughlin, M Schneier SIAM Journal on Numerical Analysis 58 (1), 565-589, 2020 | 44 | 2020 |
| Error analysis of supremizer pressure recovery for POD based reduced-order models of the time-dependent Navier--Stokes equations K Kean, M Schneier SIAM Journal on Numerical Analysis 58 (4), 2235-2264, 2020 | 42 | 2020 |
| A Leray regularized ensemble-proper orthogonal decomposition method for parameterized convection-dominated flows M Gunzburger, T Iliescu, M Schneier IMA Journal of Numerical Analysis 40 (2), 886-913, 2020 | 38 | 2020 |
| A higher-order ensemble/proper orthogonal decomposition method for the nonstationary Navier-Stokes Equations M Gunzburger, N Jiang, M Schneier International Journal of Numerical Analysis and Modeling 15, 608-627, 2018 | 33 | 2018 |
| An Evolve-Filter-Relax Stabilized Reduced Order Stochastic Collocation Method for the Time-Dependent Navier--Stokes Equations M Gunzburger, T Iliescu, M Mohebujjaman, M Schneier SIAM/ASA Journal on Uncertainty Quantification 7 (4), 1162-1184, 2019 | 28* | 2019 |
| On optimal pointwise in time error bounds and difference quotients for the proper orthogonal decomposition B Koc, S Rubino, M Schneier, J Singler, T Iliescu SIAM Journal on Numerical Analysis 59 (4), 2163-2196, 2021 | 26 | 2021 |
| An efficient, partitioned ensemble algorithm for simulating ensembles of evolutionary MHD flows at low magnetic Reynolds number N Jiang, M Schneier Numerical Methods for Partial Differential Equations 34 (6), 2129-2152, 2018 | 24 | 2018 |
| An embedded variable step IMEX scheme for the incompressible Navier–Stokes equations V DeCaria, M Schneier Computer Methods in Applied Mechanics and Engineering 376, 113661, 2021 | 20 | 2021 |
| The Scott-Vogelius Method for Stokes Problem on Anisotropic Meshes K Kean, M Neilan, M Schneier arXiv preprint arXiv:2109.14780, 2021 | 10 | 2021 |
| Diagnostics for eddy viscosity models of turbulence including data-driven/neural network based parameterizations W Layton, M Schneier Results in Applied Mathematics 8, 100099, 2020 | 9 | 2020 |
| On the Prandtl–Kolmogorov 1-equation model of turbulence K Kean, W Layton, M Schneier Philosophical Transactions of the Royal Society A 380 (2226), 20210054, 2022 | 5 | 2022 |
| Clipping over dissipation in turbulence models K Kean, W Layton, M Schneier arXiv preprint arXiv:2109.12107, 2021 | 2 | 2021 |
| Numerical integration of rational bubble functions with multiple singularities M Schneier Involve, a Journal of Mathematics 8 (2), 233-251, 2015 | 1 | 2015 |
| Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations Z Li, S Patil, F Ogoke, D Shu, W Zhen, M Schneier, JR Buchanan Jr, ... arXiv preprint arXiv:2402.17853, 2024 | | 2024 |
| An improved discrete least-squares/reduced-basis method for parameterized elliptic PDEs M Gunzburger, M Schneier, C Webster, G Zhang Journal of Scientific Computing 81, 76-91, 2019 | | 2019 |
| Ensemble Proper Orthogonal Decomposition Algorithms for the Incompressible Navier-Stokes Equations M Schneier | | 2018 |
Traian IliescuProfessor of Mathematics, Virginia TechE-mail megerősítve itt: vt.edu
