Michael Pernice

933 total citations
19 papers, 402 citations indexed

About

Michael Pernice is a scholar working on Computational Mechanics, Computational Theory and Mathematics and Numerical Analysis. According to data from OpenAlex, Michael Pernice has authored 19 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computational Mechanics, 6 papers in Computational Theory and Mathematics and 4 papers in Numerical Analysis. Recurrent topics in Michael Pernice's work include Advanced Numerical Methods in Computational Mathematics (10 papers), Computational Fluid Dynamics and Aerodynamics (7 papers) and Matrix Theory and Algorithms (5 papers). Michael Pernice is often cited by papers focused on Advanced Numerical Methods in Computational Mathematics (10 papers), Computational Fluid Dynamics and Aerodynamics (7 papers) and Matrix Theory and Algorithms (5 papers). Michael Pernice collaborates with scholars based in United States and Italy. Michael Pernice's co-authors include Homer F. Walker, Michael D. Tocci, Bobby Philip, Katherine J. Evans, D. A. Knoll, Luis Chacòn, Haiying Wang, D. Estep, Simon Tavener and Thomas Hochrainer and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and SIAM Journal on Scientific Computing.

In The Last Decade

Michael Pernice

18 papers receiving 354 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Pernice United States 9 215 128 123 42 41 19 402
Hengbin An China 10 151 0.7× 147 1.1× 155 1.3× 28 0.7× 26 0.6× 28 386
Emanuele Galligani Italy 14 237 1.1× 359 2.8× 342 2.8× 35 0.8× 26 0.6× 52 654
Daniel Ruprecht Germany 11 179 0.8× 145 1.1× 74 0.6× 18 0.4× 78 1.9× 38 398
Chang‐Yeol Jung South Korea 13 263 1.2× 267 2.1× 229 1.9× 28 0.7× 15 0.4× 64 525
T. A. Porsching United States 15 318 1.5× 121 0.9× 124 1.0× 62 1.5× 20 0.5× 60 574
Jean‐Sylvain Camier United States 7 213 1.0× 51 0.4× 96 0.8× 33 0.8× 17 0.4× 11 392
Ignacio Tomaš United States 8 280 1.3× 50 0.4× 44 0.4× 34 0.8× 53 1.3× 16 443
Milton E. Rose United States 10 266 1.2× 91 0.7× 66 0.5× 31 0.7× 36 0.9× 22 402
Mejdi Azaïez France 12 298 1.4× 58 0.5× 101 0.8× 20 0.5× 34 0.8× 50 439
Hyung‐Chun Lee South Korea 12 367 1.7× 91 0.7× 151 1.2× 40 1.0× 11 0.3× 42 550

Countries citing papers authored by Michael Pernice

Since Specialization
Citations

This map shows the geographic impact of Michael Pernice's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Pernice with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Pernice more than expected).

Fields of papers citing papers by Michael Pernice

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Pernice. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Pernice. The network helps show where Michael Pernice may publish in the future.

Co-authorship network of co-authors of Michael Pernice

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Pernice. A scholar is included among the top collaborators of Michael Pernice based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Pernice. Michael Pernice is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

19 of 19 papers shown
1.
Philip, Bobby, et al.. (2014). Dynamic implicit 3D adaptive mesh refinement for non-equilibrium radiation diffusion. Journal of Computational Physics. 262. 17–37. 8 indexed citations
2.
Rokkam, Srujan, et al.. (2014). Asymptotic and uncertainty analyses of a phase field model for void formation under irradiation. Computational Materials Science. 89. 165–175. 16 indexed citations
3.
Maljovec, Dan, Bei Wang, Valerio Pascucci, et al.. (2013). Exploration of High-Dimensional Scalar Function for Nuclear Reactor Safety Analysis and Visualization. University of North Texas Digital Library (University of North Texas). 712–723. 8 indexed citations
4.
Yang, Chao, Xiao‐Chuan Cai, David E. Keyes, & Michael Pernice. (2011). Parallel domain decomposition methods for the 3D Cahn-Hilliard equation. 4 indexed citations
5.
Estep, D., Michael Pernice, Simon Tavener, & Haiying Wang. (2010). A posteriori error analysis for a cut cell finite volume method. Computer Methods in Applied Mechanics and Engineering. 200(37-40). 2768–2781. 5 indexed citations
6.
Estep, D., et al.. (2009). A posteriori error analysis of a cell-centered finite volume method for semilinear elliptic problems. Journal of Computational and Applied Mathematics. 233(2). 459–472. 12 indexed citations
7.
Ferrarini, Luca & Michael Pernice. (2009). Modeling and control of a thermal energy system in a building automation scenario. Virtual Community of Pathological Anatomy (University of Castilla La Mancha). 238–243. 1 indexed citations
8.
Philip, Bobby, Luis Chacòn, & Michael Pernice. (2008). Implicit adaptive mesh refinement for 2D reduced resistive magnetohydrodynamics. Journal of Computational Physics. 227(20). 8855–8874. 15 indexed citations
9.
Evans, Katherine J., D. A. Knoll, & Michael Pernice. (2006). Development of a 2-D algorithm to simulate convection and phase transition efficiently. Journal of Computational Physics. 219(1). 404–417. 21 indexed citations
10.
Pernice, Michael & Bobby Philip. (2006). Solution of Equilibrium Radiation Diffusion Problems Using Implicit Adaptive Mesh Refinement. SIAM Journal on Scientific Computing. 27(5). 1709–1726. 18 indexed citations
11.
Evans, Katherine J., D. A. Knoll, & Michael Pernice. (2006). Enhanced algorithm efficiency for phase change convection using a multigrid preconditioner with a SIMPLE smoother. Journal of Computational Physics. 223(1). 121–126. 4 indexed citations
12.
Pernice, Michael & Rich Hornung. (2005). Newton-Krylov-FAC methods for problems discretized on locally refined grids. Computing and Visualization in Science. 8(2). 107–118. 5 indexed citations
13.
Vitabile, Salvatore, Michael Pernice, & Salvatore Gaglio. (2005). Daily peak temperature forecasting with Elman neural networks. Nova Science Publishers (Nova Science Publishers, Inc.). 4. 2765–2769. 5 indexed citations
14.
Pernice, Michael & Michael D. Tocci. (2001). A Multigrid-Preconditioned Newton--Krylov Method for the Incompressible Navier--Stokes Equations. SIAM Journal on Scientific Computing. 23(2). 398–418. 43 indexed citations
15.
Pernice, Michael. (2000). A hybrid multigrid method for the steady-state incompressible Navier-Stokes equations.. 10. 74–91. 7 indexed citations
16.
Pernice, Michael & Homer F. Walker. (1998). NITSOL: A Newton Iterative Solver for Nonlinear Systems. SIAM Journal on Scientific Computing. 19(1). 302–318. 213 indexed citations
17.
Zalewski, Janusz & Michael Pernice. (1996). Topics in advanced scientific computation. 4(4). 78–79. 1 indexed citations
18.
19.
Chronopoulos, A.T. & Michael Pernice. (1991). Vector Preconditioned s-Step Methods on the IBM 3090/6005/6VF. 130–137.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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