Feng‐Nan Hwang

574 citations
45 papers · 407 indexed · h-index 10

Feng‐Nan Hwang

42 papers receiving 378 citations

Peers

Feng‐Nan Hwang
Comparison fields: 5 of 58
  • Numerical Analysis 90
  • Computational Mechanics 240
  • Computational Theory and Mathematics 129
  • Statistical and Nonlinear Physics 49
  • Radiology, Nuclear Medicine and Imaging 62
Replace Antonio Leitão with:
Antonio Leitão Brazil
Anatoly B. Bakushinsky Russia
Ken Hayami Japan
R. Wienands Germany
Bryan W. Lewis United States
Tuomo Valkonen Finland
François Jauberteau France
Elena Resmerita Austria
Yat Tin Chow United States
Robert Plato Germany
Feng‐Nan Hwang relative to Antonio Leitão Brazil Antonio Leitão's profile →
Citations per field
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Citations per year

Countries citing papers authored by Feng‐Nan Hwang

Since Specialization
Citations

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

Fields of papers citing papers by Feng‐Nan Hwang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Feng‐Nan Hwang. 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 Feng‐Nan Hwang. The network helps show where Feng‐Nan Hwang may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Feng‐Nan Hwang, 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 Feng‐Nan Hwang Line = papers co-authored together Feng‐Nan Hwang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20245
2 20232
3 20233
4 20224
5 20208
6 20192
7 20198
8 20181
9 20178
10 20146
11 201420
12 20124
13 20106
14 20101
15 201017
16
Two-level nonlinear elimination based preconditioners for inexact newton methods with application in shocked duct flowcalculation
201013
17 20076
18 20074
19
Parallel fully coupled Schwarz preconditioners for saddle point problems.
20068
20 200637

About Feng‐Nan Hwang

Feng‐Nan Hwang is a scholar working on Computational Mathematics, Numerical Analysis and Computational Mechanics, having authored 45 papers that have together received 407 indexed citations. Recurring topics across this work include Advanced Numerical Methods in Computational Mathematics (21 papers), Matrix Theory and Algorithms (13 papers), Plasma Applications and Diagnostics (7 papers), Computational Fluid Dynamics and Aerodynamics (7 papers), Electromagnetic Scattering and Analysis (6 papers), Model Reduction and Neural Networks (6 papers), Plasma Diagnostics and Applications (6 papers) and Numerical methods for differential equations (5 papers). The work is most often cited by research in Numerical Analysis (90 citations), Computational Mechanics (240 citations) and Computational Theory and Mathematics (129 citations). Feng‐Nan Hwang has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Xiao‐Chuan Cai, Chin‐Te Hung, Jong‐Shinn Wu, Leopoldo P. Franca, Weichung Wang, Yongjia Yang, Chao‐An Lin, Chia‐Hui Chang, Haijian Yang and Meng‐Hsueh Chiang. Their work appears in journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and Computer Physics Communications.

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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