Dmitry Molchanov

817 total citations
11 papers, 196 citations indexed

About

Dmitry Molchanov is a scholar working on Artificial Intelligence, Mechanical Engineering and Mechanics of Materials. According to data from OpenAlex, Dmitry Molchanov has authored 11 papers receiving a total of 196 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 4 papers in Mechanical Engineering and 3 papers in Mechanics of Materials. Recurrent topics in Dmitry Molchanov's work include Adversarial Robustness in Machine Learning (4 papers), Geotechnical and Geomechanical Engineering (3 papers) and Drilling and Well Engineering (3 papers). Dmitry Molchanov is often cited by papers focused on Adversarial Robustness in Machine Learning (4 papers), Geotechnical and Geomechanical Engineering (3 papers) and Drilling and Well Engineering (3 papers). Dmitry Molchanov collaborates with scholars based in Russia. Dmitry Molchanov's co-authors include Arsenii Ashukha, Dmitry Vetrov, В. М. Зайченко and V. M. Torchinsky and has published in prestigious journals such as Journal of Physics Conference Series, Doklady Physics and arXiv (Cornell University).

In The Last Decade

Dmitry Molchanov

9 papers receiving 188 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dmitry Molchanov Russia	4	134	120	12	10	10	11	196
Luke Metz United States	5	104 0.8×	139 1.2×	17 1.4×	8 0.8×	5 0.5×	12	223
Jinmian Ye China	6	91 0.7×	79 0.7×	13 1.1×	9 0.9×	12 1.2×	7	160
Tan M. Nguyen United States	7	110 0.8×	70 0.6×	7 0.6×	10 1.0×	29 2.9×	18	174
Suraj Srinivas India	4	113 0.8×	91 0.8×	9 0.8×	10 1.0×	7 0.7×	10	166
Danlu Chen United States	2	100 0.7×	138 1.1×	7 0.6×	9 0.9×	13 1.3×	3	188
Gintare Karolina Dziugaite United Kingdom	5	88 0.7×	74 0.6×	9 0.8×	3 0.3×	7 0.7×	12	165
Xinyue Liu China	7	105 0.8×	133 1.1×	9 0.8×	6 0.6×	7 0.7×	22	214
Ligong Han United States	8	58 0.4×	207 1.7×	13 1.1×	10 1.0×	6 0.6×	19	278
Shuai Jia China	7	94 0.7×	114 0.9×	10 0.8×	6 0.6×	9 0.9×	28	200
Dong Huk Park United States	6	67 0.5×	217 1.8×	13 1.1×	9 0.9×	5 0.5×	8	267

Countries citing papers authored by Dmitry Molchanov

Since Specialization

Citations

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

Fields of papers citing papers by Dmitry Molchanov

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmitry Molchanov

This figure shows the co-authorship network connecting the top 25 collaborators of Dmitry Molchanov. A scholar is included among the top collaborators of Dmitry Molchanov 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 Dmitry Molchanov. Dmitry Molchanov 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:

11 of 11 papers shown

1.

Molchanov, Dmitry, et al.. (2021). Multivariant Well Placement and Well Drilling Parameters Optimization Methodology. Case Study from Yamal Gas Field. SPE Russian Petroleum Technology Conference. 1 indexed citations

2.

Ashukha, Arsenii, et al.. (2020). Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning. arXiv (Cornell University). 1–29. 26 indexed citations

3.

Molchanov, Dmitry, et al.. (2018). Variance Networks: When Expectation Does Not Meet Your Expectations. International Conference on Learning Representations. 1–16. 1 indexed citations

4.

Molchanov, Dmitry. (2018). The calculation of the phase equilibrium of the multicomponent hydrocarbon systems. Journal of Physics Conference Series. 946. 12114–12114.

5.

Molchanov, Dmitry, et al.. (2017). Structured Bayesian Pruning via Log-Normal Multiplicative Noise. Neural Information Processing Systems. 30. 6775–6784. 28 indexed citations

6.

Molchanov, Dmitry, Arsenii Ashukha, & Dmitry Vetrov. (2017). Variational Dropout Sparsifies Deep Neural Networks. arXiv (Cornell University). 70(70). 2498–2507. 133 indexed citations

7.

Зайченко, В. М., et al.. (2017). Two-phase filtration of multicomponent mixtures with a retrograde region of the phase diagram. Doklady Physics. 62(2). 60–62. 1 indexed citations

8.

Зайченко, В. М., et al.. (2016). Experimental study of two-phase filtration regimes of methane–n-pentane mixture. Journal of Physics Conference Series. 774. 12042–12042. 1 indexed citations

9.

Molchanov, Dmitry, et al.. (2016). Mathematical modeling of gas-condensate mixture filtration in porous media taking into account non-equilibrium of phase transitions. Journal of Physics Conference Series. 774. 12043–12043. 3 indexed citations

10.

Molchanov, Dmitry, et al.. (2015). Relevance tagging machine. 1(13). 1877–1887. 1 indexed citations

11.

Molchanov, Dmitry, et al.. (2015). Features of saturates mixture filtration in porous medium. Journal of Physics Conference Series. 653. 12108–12108. 1 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.

Explore authors with similar magnitude of impact