G. Tartakovsky
- Statistical and Nonlinear Physics top 2%
- Environmental Engineering top 5%
- Mechanical Engineering top 10%
- Civil and Structural Engineering top 10%
- Computational Mechanics top 10%
- Co-authors
- Alexandre M. TartakovskyDavid A. Barajas‐SolanoParis PerdikarisCarlos Ortiz MarreroQizhi HeShlomo P. NeumanXiu YangMichael J. Truex
- Topics
- Groundwater flow and contamination studies (7 papers)Soil and Unsaturated Flow (5 papers)Probabilistic and Robust Engineering Design (3 papers)
- Partner nations
- United StatesItaly
In The Last Decade
G. Tartakovsky
14 papers receiving 762 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Statistical and Nonlinear Physics 347
- Environmental Engineering 274
- Mechanical Engineering 170
- Civil and Structural Engineering 152
- Computational Mechanics 143
Countries citing papers authored by G. Tartakovsky
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
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
| # | Work | Indexed citations |
|---|---|---|
| 1 | 9 | |
| 2 | 1 | |
| 3 | Physics‐Informed Deep Neural Networks for Learning Parameters and Constitutive Relationships in Subsurface Flow Problemsbreakdown → | 318 |
| 4 | Physics-informed neural networks for multiphysics data assimilation with application to subsurface transportbreakdown → | 236 |
| 5 | Physics-Informed Deep Neural Networks for Multiphysics Data Assimilation in Subsurface Transport Problems | 3 |
| 6 | 10 | |
| 7 | 63 | |
| 8 | 1 | |
| 9 | Physics Informed Deep Neural Networks for learning parameters with non-Gaussian non-stationary statistics | 1 |
| 10 | 19 | |
| 11 | 7 | |
| 12 | 4 | |
| 13 | 17 | |
| 14 | 106 |
About G. Tartakovsky
G. Tartakovsky is a scholar working on Environmental Engineering, Statistics, Probability and Uncertainty and Geochemistry and Petrology, having authored 14 papers that have together received 795 indexed citations. Recurring topics across this work include Groundwater flow and contamination studies (7 papers), Soil and Unsaturated Flow (5 papers) and Probabilistic and Robust Engineering Design (3 papers). The work is most often cited by research in Statistical and Nonlinear Physics (347 citations), Environmental Engineering (274 citations) and Geophysics (134 citations). G. Tartakovsky has collaborated with scholars based in United States and Italy. Frequent 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. Their work appears in journals such as Water Resources Research, Journal of Computational Physics and Advances in Water Resources.
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.