Michael Schneier

Michael Schneier

University of Pittsburgh

H-index: 10

North America-United States

About Michael Schneier

Michael Schneier, With an exceptional h-index of 10 and a recent h-index of 10 (since 2020), a distinguished researcher at University of Pittsburgh, specializes in the field of Numerical Analysis, Reduced Order Modeling, Data Driven Algorithms, Computational Fluid Dynamics, Uncertainty Quantification.

His recent articles reflect a diverse array of research interests and contributions to the field:

Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations

On the Prandtl–Kolmogorov 1-equation model of turbulence

On optimal pointwise in time error bounds and difference quotients for the proper orthogonal decomposition

The Scott-Vogelius Method for Stokes Problem on Anisotropic Meshes

Clipping over dissipation in turbulence models

An embedded variable step IMEX scheme for the incompressible Navier–Stokes equations

Diagnostics for eddy viscosity models of turbulence including data-driven/neural network based parameterizations

A Leray regularized ensemble-proper orthogonal decomposition method for parameterized convection-dominated flows

Michael Schneier Information

University

Position

Postdoctoral Associate

Citations(all)

393

Citations(since 2020)

361

Cited By

136

hIndex(all)

10

hIndex(since 2020)

10

i10Index(all)

10

i10Index(since 2020)

10

Email

University Profile Page

Google Scholar

Michael Schneier Skills & Research Interests

Numerical Analysis

Reduced Order Modeling

Data Driven Algorithms

Computational Fluid Dynamics

Uncertainty Quantification

Top articles of Michael Schneier

Latent Neural PDE Solver: a reduced-order modelling framework for partial differential equations

arXiv preprint arXiv:2402.17853

2024/2/27

Dule Shu
Dule Shu

H-Index: 2

Michael Schneier
Michael Schneier

H-Index: 7

On the Prandtl–Kolmogorov 1-equation model of turbulence

Philosophical Transactions of the Royal Society A

2022/6/27

William Layton
William Layton

H-Index: 27

Michael Schneier
Michael Schneier

H-Index: 7

On optimal pointwise in time error bounds and difference quotients for the proper orthogonal decomposition

SIAM journal on numerical analysis, 59 (4), 2163-2196.

2021/8/5

The Scott-Vogelius Method for Stokes Problem on Anisotropic Meshes

arXiv preprint arXiv:2109.14780

2021/9/30

Michael Neilan
Michael Neilan

H-Index: 21

Michael Schneier
Michael Schneier

H-Index: 7

Clipping over dissipation in turbulence models

arXiv preprint arXiv:2109.12107

2021/9/24

William Layton
William Layton

H-Index: 27

Michael Schneier
Michael Schneier

H-Index: 7

An embedded variable step IMEX scheme for the incompressible Navier–Stokes equations

Computer Methods in Applied Mechanics and Engineering

2021/4/1

Victor Decaria
Victor Decaria

H-Index: 5

Michael Schneier
Michael Schneier

H-Index: 7

Diagnostics for eddy viscosity models of turbulence including data-driven/neural network based parameterizations

Results in Applied Mathematics

2020/11/1

William Layton
William Layton

H-Index: 27

Michael Schneier
Michael Schneier

H-Index: 7

A Leray regularized ensemble-proper orthogonal decomposition method for parameterized convection-dominated flows

IMA Journal of Numerical Analysis

2020/4/24

Error analysis of supremizer pressure recovery for POD based reduced-order models of the time-dependent Navier--Stokes equations

SIAM Journal on Numerical Analysis

2020

Michael Schneier
Michael Schneier

H-Index: 7

An artificial compression reduced order model

SIAM Journal on Numerical Analysis

2020

See List of Professors in Michael Schneier University(University of Pittsburgh)

Co-Authors

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