G. Ambika

1.4k citations
82 papers · 1.0k indexed · h-index 17

Impact in

Papers in

G. Ambika

78 papers receiving 991 citations

Peers

G. Ambika
Comparison fields: 5 of 120
  • Statistical and Nonlinear Physics 507
  • Computer Networks and Communications 414
  • Cognitive Neuroscience 131
  • Economics and Econometrics 179
  • Biological Psychiatry 14
Replace Francesco Vaccarino with:
Francesco Vaccarino Italy
R. E. Amritkar India
Jan J. Żebrowski Poland
Mikhail Ivanchenko Russia
Robert Shaw United States
Uwe an der Heiden Germany
Toru Ohira Japan
R. Eykholt United States
Б. П. Безручко Russia
В. И. Пономаренко Russia
G. Ambika relative to Francesco Vaccarino Italy Francesco Vaccarino's profile →
Citations per field
00.5×2.8×
Francesco Vaccarino · 1×
Citations per year

Countries citing papers authored by G. Ambika

Since Specialization
Citations

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

Fields of papers citing papers by G. Ambika

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 17 scholars most cited alongside G. Ambika, 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. Ambika Line = papers co-authored together G. Ambika links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20240
2 20241
3 20244
4 20230
5 202318
6 202014
7 20174
8 201634
9 20154
10 20159
11 201449
12
Suppression of dynamics in coupled discrete systems in interaction with an extended environment
20130
13 20131
14 201254
15 2011109
16 201062
17 200928
18 20076
19 20066
20 199210

About G. Ambika

G. Ambika is a scholar working on Statistical and Nonlinear Physics, Computer Networks and Communications, Mathematical Physics, Economics and Econometrics and Cognitive Neuroscience, having authored 82 papers that have together received 1.0k indexed citations. Recurring topics across this work include Nonlinear Dynamics and Pattern Formation (39 papers), Chaos control and synchronization (38 papers), Complex Systems and Time Series Analysis (26 papers), stochastic dynamics and bifurcation (20 papers), Quantum chaos and dynamical systems (18 papers), Mathematical Dynamics and Fractals (10 papers), Neural dynamics and brain function (9 papers) and Ecosystem dynamics and resilience (9 papers). The work is most often cited by research in Statistical and Nonlinear Physics (507 citations), Computer Networks and Communications (414 citations), Cognitive Neuroscience (131 citations), Economics and Econometrics (179 citations) and Biological Psychiatry (14 citations). G. Ambika has collaborated with scholars based in India, Germany and United Kingdom. Frequent co-authors include R. E. Amritkar, K. P. Harikrishnan, Ranjeev Misra, Tanvi P. Gujarati, Ajit Kembhavi, Vijayalakshmi Ravindranath, Debajyoti Das, Jürgen Kurths, G. Rangarajan and K Babu Joseph. Their work appears in journals such as Communications in Nonlinear Science and Numerical Simulation, Physics Letters A, The European Physical Journal B, Chaos An Interdisciplinary Journal of Nonlinear Science and Physica A Statistical Mechanics and its Applications.

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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