Vivek Oommen

501 total citations · 1 hit paper
10 papers, 296 citations indexed

About

Vivek Oommen is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Aerospace Engineering. According to data from OpenAlex, Vivek Oommen has authored 10 papers receiving a total of 296 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Statistical and Nonlinear Physics, 3 papers in Computational Mechanics and 3 papers in Aerospace Engineering. Recurrent topics in Vivek Oommen's work include Model Reduction and Neural Networks (7 papers), Fluid Dynamics and Turbulent Flows (3 papers) and Meteorological Phenomena and Simulations (2 papers). Vivek Oommen is often cited by papers focused on Model Reduction and Neural Networks (7 papers), Fluid Dynamics and Turbulent Flows (3 papers) and Meteorological Phenomena and Simulations (2 papers). Vivek Oommen collaborates with scholars based in United States, India and Israel. Vivek Oommen's co-authors include George Em Karniadakis, Khemraj Shukla, Rémi Dingreville, Balaji Srinivasan, Somdatta Goswami, Chenxi Wu, Juan Diego Toscano, Zongren Zou, Ameya D. Jagtap and Anindya Ghoshal and has published in prestigious journals such as Computer Methods in Applied Mechanics and Engineering, Neural Networks and Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences.

In The Last Decade

Vivek Oommen

9 papers receiving 282 citations

Hit Papers

From PINNs to PIKANs: recent advances in physics-informed... 2025 2026 2025 20 40 60
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.

  1. From PINNs to PIKANs: recent advances in physics-informed machine learning
    2025 60 citations Juan Diego Toscano, Vivek Oommen et al. profile →

Peers

Vivek Oommen
George Em Karniadakis United States
Wai Tong Chung United States
Rudy Geelen United States
Zheyuan Hu United States
Juan Diego Toscano United States
Kazuto Hasegawa Japan
Diana Alina Bistrian Romania
Stylianos Chatzidakis United States
Giorgio Gobat Italy
Stefania Fresca Italy
George Em Karniadakis United States
Vivek Oommen
Citations per year, relative to Vivek Oommen Vivek Oommen (= 1×) peers George Em Karniadakis

Countries citing papers authored by Vivek Oommen

Since Specialization
Citations

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

Fields of papers citing papers by Vivek Oommen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vivek Oommen

This figure shows the co-authorship network connecting the top 25 collaborators of Vivek Oommen. A scholar is included among the top collaborators of Vivek Oommen 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 Vivek Oommen. Vivek Oommen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Oommen, Vivek, et al.. (2025). Real-time inference and extrapolation with Time-Conditioned UNet: Applications in hypersonic flows, incompressible flows, and global temperature forecasting. Computer Methods in Applied Mechanics and Engineering. 441. 117982–117982.
2.
Toscano, Juan Diego, et al.. (2025). From PINNs to PIKANs: recent advances in physics-informed machine learning. 1(1). 60 indexed citations breakdown →
3.
Oommen, Vivek, et al.. (2025). Integrating neural operators with diffusion models improves spectral representation in turbulence modelling. Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences. 481(2309). 4 indexed citations
4.
Khodakarami, Siavash, et al.. (2025). Mitigating spectral bias in neural operators via high-frequency scaling for physical systems. Neural Networks. 193. 108027–108027. 1 indexed citations
5.
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
6.
Oommen, Vivek, Khemraj Shukla, Saaketh Desai, Rémi Dingreville, & George Em Karniadakis. (2024). Rethinking materials simulations: Blending direct numerical simulations with neural operators. npj Computational Materials. 10(1). 21 indexed citations
7.
Wu, Chenxi, et al.. (2024). GPT VS. HUMAN FOR SCIENTIFIC REVIEWS: A DUAL SOURCE REVIEW ON APPLICATIONS OF CHATGPT IN SCIENCE. 5(2). 1–44. 2 indexed citations
8.
Shukla, Khemraj, et al.. (2023). Deep neural operators as accurate surrogates for shape optimization. Engineering Applications of Artificial Intelligence. 129. 107615–107615. 46 indexed citations
9.
Oommen, Vivek, Khemraj Shukla, Somdatta Goswami, Rémi Dingreville, & George Em Karniadakis. (2022). Learning two-phase microstructure evolution using neural operators and autoencoder architectures. npj Computational Materials. 8(1). 101 indexed citations
10.
Oommen, Vivek & Balaji Srinivasan. (2022). Solving Inverse Heat Transfer Problems Without Surrogate Models: A Fast, Data-Sparse, Physics Informed Neural Network Approach. Journal of Computing and Information Science in Engineering. 22(4). 41 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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