Ameya D. Jagtap

30 papers receiving 3.3k citations

Hit Papers

Physics-informed neural networks for high-speed flows201920262021202320192020201920202022250500750

Peers

Ameya D. Jagtap
Comparison fields: 5 of 100
  • Statistical and Nonlinear Physics 2.6k
  • Computational Mechanics 1.3k
  • Aerospace Engineering 618
  • Artificial Intelligence 559
  • Mechanical Engineering 450
Replace Shengze Cai with:
Shengze Cai China
Xuhui Meng China
Andrea Manzoni Italy
Siddhartha Mishra Switzerland
Dunhui Xiao China
Zhicheng Wang China
Zhiping Mao United States
Gaëtan Kerschen Belgium
Minglang Yin United States
Benjamin Peherstorfer United States
Ameya D. Jagtap relative to Shengze Cai China Shengze Cai's profile →
Citations per field
00.5×1.5×
Shengze Cai · 1×
Citations per year

Countries citing papers authored by Ameya D. Jagtap

Since Specialization
Citations

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

Fields of papers citing papers by Ameya D. Jagtap

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ameya D. Jagtap. 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 Ameya D. Jagtap. The network helps show where Ameya D. Jagtap may publish in the future.

Co-authorship network of co-authors of Ameya D. Jagtap

This figure shows the co-authorship network connecting the top 25 collaborators of Ameya D. Jagtap. A scholar is included among the top collaborators of Ameya D. Jagtap 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 Ameya D. Jagtap. Ameya D. Jagtap 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
#WorkIndexed citations
1 6
2 10
3 3
4 10
5 20
6 34
7 31
8 68
9 81
10
Physics-informed neural networks for inverse problems in supersonic flowsbreakdown →
205
11 2
12
Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations.
1
13 84
14
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equationsbreakdown →
575
15 9
16
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problemsbreakdown →
693
17
Physics-informed neural networks for high-speed flowsbreakdown →
782
18
Adaptive activation functions accelerate convergence in deep and physics-informed neural networksbreakdown →
639
19 11
20 1

About Ameya D. Jagtap

Ameya D. Jagtap is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Statistics, Probability and Uncertainty, having authored 30 papers that have together received 3.5k indexed citations. Recurring topics across this work include Model Reduction and Neural Networks (20 papers), Fluid Dynamics and Turbulent Flows (7 papers) and Nuclear Engineering Thermal-Hydraulics (6 papers). The work is most often cited by research in Statistical and Nonlinear Physics (2.6k citations), Computational Mechanics (1.3k citations) and Statistics, Probability and Uncertainty (309 citations). Ameya D. Jagtap has collaborated with scholars based in United States, India and Singapore. Frequent co-authors include George Em Karniadakis, Zhiping Mao, Kenji Kawaguchi, Ehsan Kharazmi, Nikolaus A. Adams, Zheyuan Hu, Dimitrios Mitsotakis, Kenji Kawaguchi, Khemraj Shukla and James L. Blackshire. Their work appears in journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and IEEE Signal Processing Magazine.

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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