J. Sarich

2.3k total citations · 2 hit papers
24 papers, 1.5k citations indexed

About

J. Sarich is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, J. Sarich has authored 24 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Nuclear and High Energy Physics, 5 papers in Computer Networks and Communications and 5 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in J. Sarich's work include Nuclear physics research studies (13 papers), Astronomical and nuclear sciences (8 papers) and Distributed and Parallel Computing Systems (5 papers). J. Sarich is often cited by papers focused on Nuclear physics research studies (13 papers), Astronomical and nuclear sciences (8 papers) and Distributed and Parallel Computing Systems (5 papers). J. Sarich collaborates with scholars based in United States, Poland and Finland. J. Sarich's co-authors include N. Schunck, W. Nazarewicz, Stefan M. Wild, M. V. Stoitsov, M. Kortelainen, Jordan McDonnell, Jai More, P.‐G. Reinhard, T. Lesinski and J. Dobaczewski and has published in prestigious journals such as Physical Review Letters, Journal of Computational Chemistry and Computer Physics Communications.

In The Last Decade

J. Sarich

24 papers receiving 1.5k citations

Hit Papers

Nuclear energy density optimization 2010 2026 2015 2020 2010 2012 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Sarich United States 14 1.3k 500 206 167 156 24 1.5k
Sofia Quaglioni United States 27 2.3k 1.8× 1.3k 2.6× 426 2.1× 204 1.2× 88 0.6× 74 2.5k
Thomas Luu United States 28 1.9k 1.5× 501 1.0× 96 0.5× 88 0.5× 135 0.9× 89 2.3k
J. V. Noble United States 18 918 0.7× 635 1.3× 109 0.5× 148 0.9× 69 0.4× 82 1.3k
James S. Ball United States 28 2.2k 1.7× 535 1.1× 186 0.9× 77 0.5× 87 0.6× 97 2.7k
F. Merz Germany 23 1.3k 1.0× 246 0.5× 49 0.2× 80 0.5× 875 5.6× 53 1.6k
N. Schunck United States 34 3.4k 2.6× 1.3k 2.6× 424 2.1× 595 3.6× 241 1.5× 96 3.8k
T. T. Chou United States 18 1.7k 1.3× 287 0.6× 64 0.3× 82 0.5× 106 0.7× 42 2.0k
J. Lowe United Kingdom 23 1.0k 0.8× 515 1.0× 92 0.4× 401 2.4× 38 0.2× 79 1.3k
Kostas Orginos United States 52 7.0k 5.3× 642 1.3× 100 0.5× 84 0.5× 247 1.6× 197 7.4k
Pieter Maris United States 44 6.2k 4.7× 1.6k 3.2× 628 3.0× 196 1.2× 213 1.4× 173 6.7k

Countries citing papers authored by J. Sarich

Since Specialization
Citations

This map shows the geographic impact of J. Sarich'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 J. Sarich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Sarich more than expected).

Fields of papers citing papers by J. Sarich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J. Sarich. 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 J. Sarich. The network helps show where J. Sarich may publish in the future.

Co-authorship network of co-authors of J. Sarich

This figure shows the co-authorship network connecting the top 25 collaborators of J. Sarich. A scholar is included among the top collaborators of J. Sarich 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 J. Sarich. J. Sarich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Mahadevan, Vijay, et al.. (2020). Improving climate model coupling through a complete mesh representation: a case study with E3SM (v1) and MOAB (v5.x). Geoscientific model development. 13(5). 2355–2377. 6 indexed citations
3.
Bartlett, Roscoe, Glenn Hammond, Michael A. Heroux, et al.. (2017). xSDK Foundations: Toward an Extreme-scale Scientific Software Development Kit. Supercomputing Frontiers and Innovations. 4(1). 14 indexed citations
4.
Sarich, J., et al.. (2016). xSDK Community Installation Policies: GNU Autoconf and CMake Options. Figshare. 1 indexed citations
5.
McDonnell, Jordan, N. Schunck, David Higdon, et al.. (2015). Uncertainty Quantification for Nuclear Density Functional Theory and Information Content of New Measurements. Physical Review Letters. 114(12). 122501–122501. 89 indexed citations
6.
Wild, Stefan M., J. Sarich, & N. Schunck. (2015). Derivative-free optimization for parameter estimation in computational nuclear physics. Journal of Physics G Nuclear and Particle Physics. 42(3). 34031–34031. 17 indexed citations
7.
McDonnell, Jordan, N. Schunck, W. Nazarewicz, et al.. (2014). Uncertainty Quantification for Nuclear Density Functional Theory. Bulletin of the American Physical Society. 2014. 2 indexed citations
8.
Kortelainen, M., Jordan McDonnell, W. Nazarewicz, et al.. (2014). Nuclear energy density optimization: Shell structure. Physical Review C. 89(5). 145 indexed citations
9.
Ekström, A., C. Forssén, G. Hagen, et al.. (2013). Optimized Chiral Nucleon-Nucleon Interaction at Next-to-Next-to-Leading Order. Physical Review Letters. 110(19). 192502–192502. 224 indexed citations
10.
Holewinski, Justin, Azamat Mametjanov, Boyana Norris, et al.. (2013). Stencil-Aware GPU Optimization of Iterative Solvers. SIAM Journal on Scientific Computing. 35(5). S209–S228. 13 indexed citations
11.
Kortelainen, M., Jordan McDonnell, W. Nazarewicz, et al.. (2012). Nuclear energy density optimization: Large deformations. Physical Review C. 85(2). 305 indexed citations breakdown →
12.
Schunck, N., J. Dobaczewski, Jordan McDonnell, et al.. (2010). One-quasiparticle states in the nuclear energy density functional theory. Physical Review C. 81(2). 123 indexed citations
13.
Kortelainen, M., T. Lesinski, Jai More, et al.. (2010). Nuclear energy density optimization. Physical Review C. 82(2). 357 indexed citations breakdown →
14.
Dobaczewski, J., W. Satuła, B. G. Carlsson, et al.. (2009). Solution of the Skyrme–Hartree–Fock–Bogolyubov equations in the Cartesian deformed harmonic-oscillator basis.. Computer Physics Communications. 180(11). 2361–2391. 71 indexed citations
15.
Dolan, Elizabeth D., et al.. (2008). Kestrel: An Interface from Optimization Modeling Systems to the NEOS Server. INFORMS journal on computing. 20(4). 525–538. 14 indexed citations
16.
Morè, Jorge J., Todd Munson, & J. Sarich. (2007). Optimization in SciDAC applications. Journal of Physics Conference Series. 78. 12052–12052. 2 indexed citations
17.
Benson, Steven R., Manojkumar Krishnan, Lois Curfman McInnes, Jarek Nieplocha, & J. Sarich. (2007). Using the GA and TAO toolkits for solving large-scale optimization problems on parallel computers. ACM Transactions on Mathematical Software. 33(2). 11–11. 3 indexed citations
18.
Kenny, Joseph P., Steven J. Benson, Yuri Alexeev, et al.. (2004). Component‐based integration of chemistry and optimization software. Journal of Computational Chemistry. 25(14). 1717–1725. 26 indexed citations
19.
Kenny, Joseph P., Steven J. Benson, Yuri Alexeev, et al.. (2004). Component‐Based Integration of Chemistry and Optimization Software. ChemInform. 36(2). 1 indexed citations
20.
Benson, Steven R., Lois Curfman McInnes, Jorge J. Morè, & J. Sarich. (2004). Scalable Algorithms in Optimization: Computational Experiments. 10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. 4 indexed citations

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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