Deep Ray

1.2k total citations
24 papers, 727 citations indexed

About

Deep Ray is a scholar working on Statistical and Nonlinear Physics, Computational Mechanics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Deep Ray has authored 24 papers receiving a total of 727 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Statistical and Nonlinear Physics, 10 papers in Computational Mechanics and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in Deep Ray's work include Model Reduction and Neural Networks (15 papers), Fluid Dynamics and Turbulent Flows (7 papers) and Generative Adversarial Networks and Image Synthesis (7 papers). Deep Ray is often cited by papers focused on Model Reduction and Neural Networks (15 papers), Fluid Dynamics and Turbulent Flows (7 papers) and Generative Adversarial Networks and Image Synthesis (7 papers). Deep Ray collaborates with scholars based in United States, Switzerland and India. Deep Ray's co-authors include Jan S. Hesthaven, Qian Wang, Siddhartha Mishra, Praveen Chandrashekar, Assad A. Oberai, Dhruv Patel, Christian Rohde, Ulrik Skre Fjordholm, E. A. Johnson and Béatrice Rivière and has published in prestigious journals such as Scientific Reports, Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Deep Ray

23 papers receiving 693 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Deep Ray United States 11 448 370 122 110 99 24 727
Youngsoo Choi United States 16 581 1.3× 317 0.9× 229 1.9× 104 0.9× 71 0.7× 35 809
Jens Berg Sweden 9 419 0.9× 279 0.8× 84 0.7× 57 0.5× 95 1.0× 11 656
Bing Yu China 4 655 1.5× 293 0.8× 119 1.0× 67 0.6× 160 1.6× 9 848
Jean-Christophe Loiseau France 15 403 0.9× 473 1.3× 123 1.0× 170 1.5× 113 1.1× 26 903
Eurika Kaiser United States 10 529 1.2× 355 1.0× 128 1.0× 177 1.6× 139 1.4× 25 912
Mengwu Guo Netherlands 11 477 1.1× 213 0.6× 279 2.3× 123 1.1× 66 0.7× 22 746
Kookjin Lee United States 9 302 0.7× 161 0.4× 88 0.7× 60 0.5× 72 0.7× 26 462
Suraj Pawar United States 13 287 0.6× 246 0.7× 52 0.4× 106 1.0× 66 0.7× 34 556
David A. Barajas‐Solano United States 12 461 1.0× 177 0.5× 145 1.2× 76 0.7× 143 1.4× 31 887
Gahl Berkooz Sweden 2 324 0.7× 365 1.0× 118 1.0× 130 1.2× 29 0.3× 2 606

Countries citing papers authored by Deep Ray

Since Specialization
Citations

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

Fields of papers citing papers by Deep Ray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deep Ray

This figure shows the co-authorship network connecting the top 25 collaborators of Deep Ray. A scholar is included among the top collaborators of Deep Ray 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 Deep Ray. Deep Ray 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
1.
Ray, Deep, et al.. (2025). Learning WENO for Entropy Stable Schemes to Solve Conservation Laws. SIAM Journal on Scientific Computing. 47(6). C1196–C1222.
2.
Ray, Deep, Bino Varghese, Darryl Hwang, et al.. (2024). Conditional generative learning for medical image imputation. Scientific Reports. 14(1). 171–171. 6 indexed citations
3.
Ray, Deep, et al.. (2024). Deep Learning and Computational Physics. 1 indexed citations
4.
5.
Patel, Dhruv, et al.. (2023). Variationally mimetic operator networks. Computer Methods in Applied Mechanics and Engineering. 419. 116536–116536. 10 indexed citations
6.
Ray, Deep, et al.. (2023). Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty. Computer Methods in Applied Mechanics and Engineering. 417. 116338–116338. 10 indexed citations
7.
Ray, Deep, et al.. (2022). The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems. Numerical Algebra Control and Optimization. 14(1). 160–189. 9 indexed citations
8.
Patel, Dhruv, Deep Ray, Bino Varghese, et al.. (2022). Probabilistic medical image imputation via deep adversarial learning. Engineering With Computers. 38(5). 3975–3986. 2 indexed citations
9.
Patel, Dhruv, Deep Ray, & Assad A. Oberai. (2022). Solution of physics-based Bayesian inverse problems with deep generative priors. Computer Methods in Applied Mechanics and Engineering. 400. 115428–115428. 30 indexed citations
10.
Ray, Deep, et al.. (2021). Controlling oscillations in spectral methods by local artificial viscosity governed by neural networks. Journal of Computational Physics. 431. 110144–110144. 18 indexed citations
11.
Hesthaven, Jan S., et al.. (2020). Controlling oscillations in high-order Discontinuous Galerkin schemes using artificial viscosity tuned by neural networks. Journal of Computational Physics. 409. 109304–109304. 44 indexed citations
12.
Patel, Dhruv, et al.. (2020). Bayesian Inference in Physics-Driven Problems with Adversarial Priors. 2 indexed citations
13.
Ray, Deep, et al.. (2020). Constraint-aware neural networks for Riemann problems. Journal of Computational Physics. 409. 109345–109345. 39 indexed citations
14.
Mishra, Siddhartha, et al.. (2020). Deep learning observables in computational fluid dynamics. Journal of Computational Physics. 410. 109339–109339. 100 indexed citations
15.
Mishra, Siddhartha, et al.. (2020). Iterative surrogate model optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks. Computer Methods in Applied Mechanics and Engineering. 374. 113575–113575. 51 indexed citations
16.
Ray, Deep & Jan S. Hesthaven. (2019). Detecting troubled-cells on two-dimensional unstructured grids using a neural network. Journal of Computational Physics. 397. 108845–108845. 43 indexed citations
17.
Wang, Qian, Jan S. Hesthaven, & Deep Ray. (2019). Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem. Journal of Computational Physics. 384. 289–307. 188 indexed citations
18.
Ray, Deep & Jan S. Hesthaven. (2018). An artificial neural network as a troubled-cell indicator. Journal of Computational Physics. 367. 166–191. 95 indexed citations
19.
Ray, Deep & Praveen Chandrashekar. (2017). An entropy stable finite volume scheme for the two dimensional Navier–Stokes equations on triangular grids. Applied Mathematics and Computation. 314. 257–286. 7 indexed citations
20.
Ray, Deep, Praveen Chandrashekar, Ulrik Skre Fjordholm, & Siddhartha Mishra. (2016). Entropy Stable Scheme on Two-Dimensional Unstructured Grids for Euler Equations. Communications in Computational Physics. 19(5). 1111–1140. 34 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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