G. Kluth

1.0k citations
12 papers · 149 · h-index 5

Impact in

Papers in

    • Computational Fluid Dynamics and Aerodynamics 4
    • Advanced Numerical Methods in Computational Mathematics 2
    • Fluid Dynamics and Turbulent Flows 2
    • Meteorological Phenomena and Simulations 2

G. Kluth

12 papers receiving 147 citations

Peers

G. Kluth
Comparison fields: 5 of 34
  • Computational Mechanics 80
  • Nuclear and High Energy Physics 50
  • Applied Mathematics 22
  • Geophysics 22
  • Numerical Analysis 7
Replace Peng Song with:
Peng Song China
Hai Le United States
V.N. Mokhov Russia
J. Ovadia France
D. Rapagnani Italy
В. В. Скворцов Russia
J. M. Scott United States
C. J. Pawley United States
Heath L. Hanshaw United States
C. Cherfils-Clérouin France
G. Kluth relative to Peng Song China Peng Song's profile →
Citations per field
00.5×2.6×
Peng Song · 1×
Citations per year

Countries citing papers authored by G. Kluth

Since Specialization
Citations

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

Fields of papers citing papers by G. Kluth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside G. Kluth, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with G. Kluth Line = papers co-authored together G. Kluth links everyone, so they are left out of the graph.

All Works

12 of 12 papers shown
#Work
1 201055
2 201830
3 202028
4 200812
5 20185
6 20224
7 20194
8 20084
9 20253
10 20222
11
Deep Learning for Non-Local Thermodynamic Equilibrium in hydrocodes for ICF
20191
12 20221

About G. Kluth

G. Kluth is a scholar working on Computational Mechanics, Atmospheric Science, Nuclear and High Energy Physics, Astronomy and Astrophysics and Geophysics, having authored 12 papers that have together received 149 indexed citations. Recurring topics across this work include Computational Fluid Dynamics and Aerodynamics (4 papers), Laser-Plasma Interactions and Diagnostics (3 papers), Advanced Numerical Methods in Computational Mathematics (2 papers), Meteorological Phenomena and Simulations (2 papers), Ionosphere and magnetosphere dynamics (2 papers), Magnetic confinement fusion research (2 papers), Elasticity and Material Modeling (2 papers) and Fluid Dynamics and Turbulent Flows (2 papers). The work is most often cited by research in Computational Mechanics (80 citations), Nuclear and High Energy Physics (50 citations), Applied Mathematics (22 citations), Geophysics (22 citations) and Numerical Analysis (7 citations). G. Kluth has collaborated with scholars based in France and United States. Frequent co-authors include Bruno Després, P. E. Masson-Laborde, C. V. Young, M. V. Patel, L. Divol, J. M. Koning, S. Laffite, J. L. Peterson, Kelli Humbird and H. A. Scott. Their work appears in journals such as Physics of Plasmas, Journal of Computational Physics, Journal of Fluid Mechanics, Continuum Mechanics and Thermodynamics and Nuclear Fusion.

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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