William Layton

William Layton

University of Pittsburgh

H-index: 49

North America-United States

About William Layton

William Layton, With an exceptional h-index of 49 and a recent h-index of 29 (since 2020), a distinguished researcher at University of Pittsburgh, specializes in the field of computational mathematics, analysis of numerical methods, turbulence.

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

The Ramshaw-Mesina Hybrid Algorithm applied to the Navier Stokes Equations

Convergence of a Ramshaw-Mesina iteration

Time step adaptivity in the method of Dahlquist, Liniger and Nevanlinna

On a 1/2-equation model of turbulence

Analysis of the variable step method of Dahlquist, Liniger and Nevanlinna for fluid flow

A general linear method approach to the design and optimization of efficient, accurate, and easily implemented time-stepping methods in CFD

Stability in 3d of a sparse grad-div approximation of the Navier-Stokes equations

On the Prandtl–Kolmogorov 1-equation model of turbulence

William Layton Information

University

Position

Professor

Citations(all)

8248

Citations(since 2020)

2730

Cited By

8485

hIndex(all)

49

hIndex(since 2020)

29

i10Index(all)

125

i10Index(since 2020)

70

Email

University Profile Page

University of Pittsburgh

Google Scholar

View Google Scholar Profile

William Layton Skills & Research Interests

computational mathematics

analysis of numerical methods

turbulence

Top articles of William Layton

Title

Journal

Author(s)

Publication Date

The Ramshaw-Mesina Hybrid Algorithm applied to the Navier Stokes Equations

arXiv preprint arXiv:2404.11755

Aytekin Çibik

Farjana Siddiqua

William Layton

2024/4/17

Convergence of a Ramshaw-Mesina iteration

Applied Mathematics Letters

Aytekin Çıbık

William Layton

2024/4/15

Time step adaptivity in the method of Dahlquist, Liniger and Nevanlinna

Advances in Computational Science and Engineering

William Layton

Wenlong Pei

Catalin Trenchea

2023/9/1

On a 1/2-equation model of turbulence

arXiv preprint arXiv:2309.03358

Rui Fang

Weiwei Han

William Layton

2023/9/6

Analysis of the variable step method of Dahlquist, Liniger and Nevanlinna for fluid flow

Numerical Methods for Partial Differential Equations

William Layton

Wenlong Pei

Yi Qin

Catalin Trenchea

2022/11

A general linear method approach to the design and optimization of efficient, accurate, and easily implemented time-stepping methods in CFD

Journal of Computational Physics

Victor DeCaria

Sigal Gottlieb

Zachary J Grant

William J Layton

2022/4/15

Stability in 3d of a sparse grad-div approximation of the Navier-Stokes equations

Journal of Mathematical Analysis and Applications

William Layton

Shuxian Xu

2022/12/1

On the Prandtl–Kolmogorov 1-equation model of turbulence

Philosophical Transactions of the Royal Society A

Kiera Kean

William Layton

Michael Schneier

2022/6/27

Refactorization of a variable step, unconditionally stable method of Dahlquist, Liniger and Nevanlinna

Applied Mathematics Letters

William Layton

Wenlong Pei

Catalin Trenchea

2022/3/1

Time filters and spurious acoustics in artificial compression methods

Numerical Methods for Partial Differential Equations

Ahmet Guzel

William Layton

Michael McLaughlin

Yao Rong

2022/11

Conditioning of linear systems arising from penalty methods

arXiv preprint arXiv:2206.06971

William Layton

Shuxian Xu

2022/6/14

Clipping over dissipation in turbulence models

arXiv preprint arXiv:2109.12107

Kiera Kean

William Layton

Michael Schneier

2021/9/24

A variable stepsize, variable order family of low complexity

SIAM Journal on Scientific Computing

Victor DeCaria

Ahmet Guzel

William Layton

Yi Li

2021

Numerical linear algebra

Lloyd N Trefethen

David Bau

2022/6/17

Adaptive partitioned methods for the time-accurate approximation of the evolutionary Stokes–Darcy system

Computer Methods in Applied Mechanics and Engineering

Yi Li

Yanren Hou

William Layton

Haiyun Zhao

2020/6/1

On the foundations of eddy viscosity models of turbulence

Fluids

Nan Jiang

William Layton

Michael McLaughlin

Yao Rong

Haiyun Zhao

2020

An artificial compression reduced order model

SIAM Journal on Numerical Analysis

Victor DeCaria

Traian Iliescu

William Layton

Michael McLaughlin

Michael Schneier

2020

Doubly-adaptive artificial compression methods for incompressible flow

Journal of Numerical Mathematics

William Layton

Michael McLaughlin

2020/9/25

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

Results in Applied Mathematics

William Layton

Michael Schneier

2020/11/1

See List of Professors in William Layton University(University of Pittsburgh)