G. Tartakovsky

1.4k total citations · 2 hit papers
14 papers, 795 citations indexed

About

G. Tartakovsky is a scholar working on Environmental Engineering, Civil and Structural Engineering and Ocean Engineering. According to data from OpenAlex, G. Tartakovsky has authored 14 papers receiving a total of 795 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Environmental Engineering, 6 papers in Civil and Structural Engineering and 4 papers in Ocean Engineering. Recurrent topics in G. Tartakovsky's work include Groundwater flow and contamination studies (7 papers), Soil and Unsaturated Flow (5 papers) and Probabilistic and Robust Engineering Design (3 papers). G. Tartakovsky is often cited by papers focused on Groundwater flow and contamination studies (7 papers), Soil and Unsaturated Flow (5 papers) and Probabilistic and Robust Engineering Design (3 papers). G. Tartakovsky collaborates with scholars based in United States and Italy. G. Tartakovsky's co-authors include Alexandre M. Tartakovsky, David A. Barajas‐Solano, Paris Perdikaris, Carlos Ortiz Marrero, Qizhi He, Shlomo P. Neuman, Xiu Yang, Michael J. Truex, Alberto Guadagnini and Mart Oostrom and has published in prestigious journals such as Water Resources Research, Journal of Computational Physics and Advances in Water Resources.

In The Last Decade

G. Tartakovsky

14 papers receiving 762 citations

Hit Papers

Physics‐Informed Deep Neural Networks for Learning Parame... 2020 2026 2022 2024 2020 2020 100 200 300
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. Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems
    2020 318 citations Alexandre M. Tartakovsky, Carlos Ortiz Marrero et al. Water Resources Research profile →
  2. Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport
    2020 236 citations Qizhi He, David A. Barajas‐Solano et al. Advances in Water Resources profile →

Peers

G. Tartakovsky
David A. Barajas‐Solano United States
Nanzhe Wang China
Haibin Chang China
George Shu Heng Pau United States
Sergey Oladyshkin Germany
Junsheng Zeng China
Sergio Zlotnik Spain
My Ha Dao Singapore
Dongxu Han China
Gege Wen United States
David A. Barajas‐Solano United States
G. Tartakovsky
Citations per year, relative to G. Tartakovsky G. Tartakovsky (= 1×) peers David A. Barajas‐Solano

Countries citing papers authored by G. Tartakovsky

Since Specialization
Citations

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

Fields of papers citing papers by G. Tartakovsky

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G. Tartakovsky

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

All Works

14 of 14 papers shown
1.
Yang, Xiu, G. Tartakovsky, & Alexandre M. Tartakovsky. (2021). Physics Information Aided Kriging using Stochastic Simulation Models. SIAM Journal on Scientific Computing. 43(6). A3862–A3891. 9 indexed citations
2.
Johnson, Christian D., et al.. (2021). A Rapid Decision Support Tool for Estimating Impacts of a Vadose Zone Volatile Organic Compound Source on Groundwater and Soil Gas. Groundwater Monitoring & Remediation. 42(1). 81–87. 1 indexed citations
3.
Tartakovsky, Alexandre M., Carlos Ortiz Marrero, Paris Perdikaris, G. Tartakovsky, & David A. Barajas‐Solano. (2020). Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problems. Water Resources Research. 56(5). 318 indexed citations breakdown →
4.
He, Qizhi, David A. Barajas‐Solano, G. Tartakovsky, & Alexandre M. Tartakovsky. (2020). Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport. Advances in Water Resources. 141. 103610–103610. 236 indexed citations breakdown →
5.
He, Qizhi, G. Tartakovsky, David A. Barajas‐Solano, & Alexandre M. Tartakovsky. (2019). Physics-Informed Deep Neural Networks for Multiphysics Data Assimilation in Subsurface Transport Problems. AGU Fall Meeting Abstracts. 2019. 3 indexed citations
6.
Yonkofski, Catherine, et al.. (2019). Risk-based monitoring designs for detecting CO2 leakage through abandoned wellbores: An application of NRAP’s WLAT and DREAM tools. International journal of greenhouse gas control. 91. 102807–102807. 10 indexed citations
7.
Yang, Xiu, David A. Barajas‐Solano, G. Tartakovsky, & Alexandre M. Tartakovsky. (2019). Physics-informed CoKriging: A Gaussian-process-regression-based multifidelity method for data-model convergence. Journal of Computational Physics. 395. 410–431. 63 indexed citations
8.
Scheibe, Tim, Xingyuan Chen, James Stegen, et al.. (2018). Data-Model Integration for Improved Prediction of River Corridor and Watershed Function. 1 indexed citations
9.
Tartakovsky, G., Alexandre M. Tartakovsky, & Paris Perdikaris. (2018). Physics Informed Deep Neural Networks for learning parameters with non-Gaussian non-stationary statistics. AGU Fall Meeting Abstracts. 2018. 1 indexed citations
10.
Tartakovsky, Alexandre M., et al.. (2017). Uncertainty Quantification in Scale‐Dependent Models of Flow in Porous Media. Water Resources Research. 53(11). 9392–9401. 19 indexed citations
11.
Oostrom, M., Michael J. Truex, Vince R. Vermeul, et al.. (2014). Remedial Amendment Delivery Near the Water Table Using Shear Thinning Fluids: Experiments and Numerical Simulations. Environmental Processes. 1(4). 331–351. 7 indexed citations
12.
Oostrom, M., G. Tartakovsky, T. W. Wietsma, Michael J. Truex, & J. H. Dane. (2011). Determination of Water Saturation in Relatively Dry Porous Media Using Gas‐Phase Tracer Tests. Vadose Zone Journal. 10(2). 634–641. 4 indexed citations
13.
Oostrom, Mart, Michael J. Truex, G. Tartakovsky, & T. W. Wietsma. (2010). Three‐Dimensional Simulation of Volatile Organic Compound Mass Flux from the Vadose Zone to Groundwater. Groundwater Monitoring & Remediation. 30(3). 45–56. 17 indexed citations
14.
Tartakovsky, G. & Shlomo P. Neuman. (2007). Three‐dimensional saturated‐unsaturated flow with axial symmetry to a partially penetrating well in a compressible unconfined aquifer. Water Resources Research. 43(1). 106 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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