Ameya D. Jagtap

5.3k total citations · 5 hit papers
30 papers, 3.5k citations indexed

About

Ameya D. Jagtap is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Aerospace Engineering. According to data from OpenAlex, Ameya D. Jagtap has authored 30 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Statistical and Nonlinear Physics, 12 papers in Computational Mechanics and 6 papers in Aerospace Engineering. Recurrent topics in Ameya D. Jagtap's work include Model Reduction and Neural Networks (20 papers), Fluid Dynamics and Turbulent Flows (7 papers) and Nuclear Engineering Thermal-Hydraulics (6 papers). Ameya D. Jagtap is often cited by papers focused on Model Reduction and Neural Networks (20 papers), Fluid Dynamics and Turbulent Flows (7 papers) and Nuclear Engineering Thermal-Hydraulics (6 papers). Ameya D. Jagtap collaborates with scholars based in United States, India and Singapore. Ameya D. Jagtap's 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 and has published in prestigious journals such as Journal of Computational Physics, Computer Methods in Applied Mechanics and Engineering and IEEE Signal Processing Magazine.

In The Last Decade

Ameya D. Jagtap

30 papers receiving 3.3k citations

Hit Papers

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What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Physics-informed neural networks for high-speed flows

2019 782 citations Zhiping Mao, Ameya D. Jagtap et al. Computer Methods in Applied Mechanics and Engineering profile →
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

2020 693 citations Ameya D. Jagtap, Ehsan Kharazmi et al. Computer Methods in Applied Mechanics and Engineering profile →
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

2019 639 citations Ameya D. Jagtap, Kenji Kawaguchi et al. Journal of Computational Physics profile →
Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

2020 575 citations Ameya D. Jagtap, George Em Karniadakis Communications in Computational Physics profile →
Physics-informed neural networks for inverse problems in supersonic flows

2022 205 citations Ameya D. Jagtap, Zhiping Mao et al. arXiv (Cornell University) profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ameya D. Jagtap United States	15	2.6k	1.3k	618	559	450	30	3.5k
Shengze Cai China	17	2.1k 0.8×	1.4k 1.1×	647 1.0×	461 0.8×	478 1.1×	44	3.5k
Xuhui Meng China	18	1.7k 0.7×	862 0.7×	462 0.7×	506 0.9×	315 0.7×	35	2.8k
Zhiping Mao United States	18	1.6k 0.6×	927 0.7×	436 0.7×	354 0.6×	306 0.7×	37	3.1k
Andrea Manzoni Italy	32	2.2k 0.8×	1.5k 1.1×	338 0.5×	165 0.3×	263 0.6×	124	3.7k
Zhicheng Wang China	9	1.1k 0.4×	533 0.4×	318 0.5×	300 0.5×	301 0.7×	25	2.0k
Benjamin Peherstorfer United States	22	1.6k 0.6×	732 0.6×	427 0.7×	287 0.5×	266 0.6×	63	2.8k
Dunhui Xiao China	29	1.1k 0.4×	773 0.6×	338 0.5×	147 0.3×	202 0.4×	68	2.2k
Karthik Duraisamy United States	25	1.7k 0.6×	2.5k 2.0×	1.2k 2.0×	208 0.4×	322 0.7×	108	3.7k
Shervin Bagheri Sweden	24	1.6k 0.6×	2.6k 2.1×	1.0k 1.6×	121 0.2×	399 0.9×	66	3.5k
Siddhartha Mishra Switzerland	33	749 0.3×	1.6k 1.3×	264 0.4×	256 0.5×	684 1.5×	129	3.5k

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

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Moseley, Ben, et al.. (2025). History-Matching of imbibition flow in fractured porous media Using Physics-Informed Neural Networks (PINNs). Computer Methods in Applied Mechanics and Engineering. 437. 117784–117784. 10 indexed citations

Menon, Suresh & Ameya D. Jagtap. (2025). Anant-Net: Breaking the curse of dimensionality with scalable and interpretable neural surrogate for high-dimensional PDEs. Computer Methods in Applied Mechanics and Engineering. 447. 118403–118403. 3 indexed citations

Jagtap, Ameya D., et al.. (2025). Challenges and advancements in modeling shock fronts with physics-informed neural networks: A review and benchmarking study. Neurocomputing. 657. 131440–131440. 6 indexed citations

Sharma, Ankush, et al.. (2024). Large language model-based evolutionary optimizer: Reasoning with elitism. Neurocomputing. 622. 129272–129272. 10 indexed citations

Oommen, Vivek, et al.. (2024). RiemannONets: Interpretable neural operators for Riemann problems. Computer Methods in Applied Mechanics and Engineering. 426. 116996–116996. 20 indexed citations

Jagtap, Ameya D., et al.. (2023). A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions. Journal of Computational Physics. 493. 112464–112464. 34 indexed citations

Goswami, Somdatta, et al.. (2023). Learning stiff chemical kinetics using extended deep neural operators. Computer Methods in Applied Mechanics and Engineering. 419. 116674–116674. 31 indexed citations

Jagtap, Ameya D. & George Em Karniadakis. (2023). HOW IMPORTANT ARE ACTIVATION FUNCTIONS IN REGRESSION AND CLASSIFICATION? A SURVEY, PERFORMANCE COMPARISON, AND FUTURE DIRECTIONS. 4(1). 21–75. 68 indexed citations

Jagtap, Ameya D., Zhiping Mao, Nikolaus A. Adams, & George Em Karniadakis. (2022). Physics-informed neural networks for inverse problems in supersonic flows. arXiv (Cornell University). 205 indexed citations breakdown →

10.

Jagtap, Ameya D., Dimitrios Mitsotakis, & George Em Karniadakis. (2022). Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations. Ocean Engineering. 248. 110775–110775. 67 indexed citations

11.

Jagtap, Ameya D., Zhiping Mao, Nikolaus A. Adams, & George Em Karniadakis. (2022). Physics-Informed Neural Networks for Inverse Problems in Supersonic Flows. Journal of Computational Physics. 466. 111402–111402. 2 indexed citations

12.

Jagtap, Ameya D. & George Em Karniadakis. (2021). Extended Physics-informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition based Deep Learning Framework for Nonlinear Partial Differential Equations.. National Conference on Artificial Intelligence. 1 indexed citations

13.

Shukla, Khemraj, et al.. (2021). A Physics-Informed Neural Network for Quantifying the Microstructural Properties of Polycrystalline Nickel Using Ultrasound Data: A promising approach for solving inverse problems. IEEE Signal Processing Magazine. 39(1). 68–77. 84 indexed citations

14.

Jagtap, Ameya D. & George Em Karniadakis. (2020). Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations. Communications in Computational Physics. 28(5). 2002–2041. 575 indexed citations breakdown →

15.

Jagtap, Ameya D., Ehsan Kharazmi, & George Em Karniadakis. (2020). Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems. Computer Methods in Applied Mechanics and Engineering. 365. 113028–113028. 693 indexed citations breakdown →

16.

Mao, Zhiping, Ameya D. Jagtap, & George Em Karniadakis. (2019). Physics-informed neural networks for high-speed flows. Computer Methods in Applied Mechanics and Engineering. 360. 112789–112789. 782 indexed citations breakdown →

17.

Jagtap, Ameya D., Kenji Kawaguchi, & George Em Karniadakis. (2019). Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. Journal of Computational Physics. 404. 109136–109136. 639 indexed citations breakdown →

18.

Jagtap, Ameya D.. (2018). Method of relaxed streamline upwinding for hyperbolic conservation laws. Wave Motion. 78. 132–161. 1 indexed citations

19.

Jagtap, Ameya D. & A. S. Vasudeva Murthy. (2018). Higher order scheme for two-dimensional inhomogeneous sine-Gordon equation with impulsive forcing. Communications in Nonlinear Science and Numerical Simulation. 64. 178–197. 11 indexed citations

20.

Jagtap, Ameya D. & A. S. Vasudeva Murthy. (2018). Higher order spectral element scheme for two- and three-dimensional Cahn–Hilliard equation. International Journal of Advances in Engineering Sciences and Applied Mathematics. 10(1). 79–89. 2 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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