Alexandre M. Tartakovsky

7.9k total citations · 3 hit papers
167 papers, 5.7k citations indexed

About

Alexandre M. Tartakovsky is a scholar working on Computational Mechanics, Environmental Engineering and Statistics, Probability and Uncertainty. According to data from OpenAlex, Alexandre M. Tartakovsky has authored 167 papers receiving a total of 5.7k indexed citations (citations by other indexed papers that have themselves been cited), including 74 papers in Computational Mechanics, 52 papers in Environmental Engineering and 28 papers in Statistics, Probability and Uncertainty. Recurrent topics in Alexandre M. Tartakovsky's work include Fluid Dynamics Simulations and Interactions (45 papers), Groundwater flow and contamination studies (44 papers) and Lattice Boltzmann Simulation Studies (44 papers). Alexandre M. Tartakovsky is often cited by papers focused on Fluid Dynamics Simulations and Interactions (45 papers), Groundwater flow and contamination studies (44 papers) and Lattice Boltzmann Simulation Studies (44 papers). Alexandre M. Tartakovsky collaborates with scholars based in United States, Germany and Spain. Alexandre M. Tartakovsky's co-authors include Paul Meakin, Tim Scheibe, David A. Barajas‐Solano, Daniel M. Tartakovsky, G. Tartakovsky, Qizhi He, Ilenia Battiato, Pietro de Anna, Paris Perdikaris and Wenxiao Pan and has published in prestigious journals such as Physical Review Letters, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Alexandre M. Tartakovsky

158 papers receiving 5.5k citations

Hit Papers

align trajectories sort by overperformance

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.

Modeling and simulation of pore‐scale multiphase fluid flow and reactive transport in fractured and porous media

2009 321 citations Alexandre M. Tartakovsky et al. profile →
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 →
Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport

2020 236 citations Qizhi He, David A. Barajas‐Solano et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Alexandre M. Tartakovsky United States	40	2.2k	1.9k	1.3k	846	789	167	5.7k
Hrvoje Jasak Croatia	31	5.5k 2.5×	1.1k 0.6×	1.4k 1.1×	1.2k 1.4×	721 0.9×	142	8.7k
Alex Hansen Norway	42	1.4k 0.6×	818 0.4×	1.3k 1.0×	937 1.1×	1.8k 2.3×	273	6.9k
Gianluca Iaccarino United States	45	7.6k 3.5×	1.9k 1.0×	846 0.6×	912 1.1×	394 0.5×	280	10.4k
Patrick J. Roache United States	28	5.0k 2.3×	1.5k 0.8×	839 0.6×	1.6k 1.9×	789 1.0×	87	8.9k
Rubén Juanes United States	52	2.2k 1.0×	4.2k 2.2×	3.4k 2.6×	2.9k 3.4×	2.4k 3.1×	216	10.0k
Cass T. Miller United States	49	2.6k 1.2×	3.7k 1.9×	2.3k 1.7×	1.2k 1.4×	868 1.1×	214	8.1k
Omar Knio United States	38	1.6k 0.7×	669 0.3×	273 0.2×	1.3k 1.5×	1.0k 1.3×	218	6.0k
Rainer Helmig Germany	44	1.9k 0.9×	3.2k 1.7×	1.6k 1.2×	1.7k 2.0×	1.0k 1.3×	219	6.5k
Gavin Tabor United Kingdom	26	3.9k 1.8×	1.1k 0.6×	718 0.5×	819 1.0×	385 0.5×	88	6.4k
Milovan Perić Germany	26	7.9k 3.6×	1.6k 0.8×	1.5k 1.1×	2.3k 2.7×	786 1.0×	77	12.2k

Countries citing papers authored by Alexandre M. Tartakovsky

Since Specialization

Citations

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

Fields of papers citing papers by Alexandre M. Tartakovsky

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexandre M. Tartakovsky

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

All Works

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20 of 20 papers shown

Barajas‐Solano, David A., et al.. (2025). Randomized physics-informed neural networks for Bayesian data assimilation. Computer Methods in Applied Mechanics and Engineering. 436. 117670–117670.

Wang, Hongsheng, et al.. (2024). A deep learning-based workflow for fast prediction of 3D state variables in geological carbon storage: A dimension reduction approach. Journal of Hydrology. 636. 131219–131219. 10 indexed citations

Jamil, Ahsan, Dale F. Rucker, Dan Lu, et al.. (2024). Comparison of machine learning and electrical resistivity arrays to inverse modeling for locating and characterizing subsurface targets. Journal of Applied Geophysics. 229. 105493–105493. 4 indexed citations

Barajas‐Solano, David A., et al.. (2024). Gaussian process regression and conditional Karhunen-Loève models for data assimilation in inverse problems. Journal of Computational Physics. 502. 112788–112788. 1 indexed citations

Saksena, Siddharth, et al.. (2023). Urban Flood Modeling: Uncertainty Quantification and Physics‐Informed Gaussian Processes Regression Forecasting. Water Resources Research. 59(3). 16 indexed citations

Ganesh, M., et al.. (2023). A STOCHASTIC DOMAIN DECOMPOSITION AND POST-PROCESSING ALGORITHM FOR EPISTEMIC UNCERTAINTY QUANTIFICATION. International Journal for Uncertainty Quantification. 13(5). 1–22. 1 indexed citations

Tartakovsky, Alexandre M., et al.. (2023). Physics-informed machine learning method with space-time Karhunen-Loève expansions for forward and inverse partial differential equations. Journal of Computational Physics. 499. 112723–112723. 4 indexed citations

He, Qizhi, Panos Stinis, & Alexandre M. Tartakovsky. (2022). Physics-constrained deep neural network method for estimating parameters in a redox flow battery. Journal of Power Sources. 528. 231147–231147. 32 indexed citations

Barajas‐Solano, David A., et al.. (2022). Physics‐Informed Machine Learning Method for Large‐Scale Data Assimilation Problems. Water Resources Research. 58(5). 21 indexed citations

10.

Kordilla, Jannes, Marco Dentz, & Alexandre M. Tartakovsky. (2021). Numerical and Analytical Modeling of Flow Partitioning in Partially Saturated Fracture Networks. Water Resources Research. 57(4). 7 indexed citations

11.

Otero‐Muras, Irene, et al.. (2020). CRNT4SBML: a Python package for the detection of bistability in biochemical reaction networks. Bioinformatics. 36(12). 3922–3924. 4 indexed citations

12.

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 →

13.

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

14.

Barajas‐Solano, David A. & Alexandre M. Tartakovsky. (2019). Approximate Bayesian model inversion for PDEs with heterogeneous and state-dependent coefficients. Journal of Computational Physics. 395. 247–262. 12 indexed citations

15.

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

16.

Kordilla, Jannes, et al.. (2017). Effect of Unsaturated Flow Modes on Partitioning Dynamics of Gravity‐Driven Flow at a Simple Fracture Intersection: Laboratory Study and Three‐Dimensional Smoothed Particle Hydrodynamics Simulations. Water Resources Research. 53(11). 9496–9518. 24 indexed citations

17.

Kordilla, Jannes, Tobias Geyer, & Alexandre M. Tartakovsky. (2012). Simulation of film and droplet flow on wide aperture fractures using Smoothed Particle Hydrodynamics. EGUGA. 1490. 1 indexed citations

18.

Balter, Ariel & Alexandre M. Tartakovsky. (2011). Multinomial diffusion equation. Physical Review E. 83(6). 61143–61143. 1 indexed citations

19.

Battiato, Ilenia, et al.. (2008). Mixing-Induced Precipitation Phenomena: Range of Applicability of Macroscopic Equations. AGUFM. 2008.

20.

Redden, G. D., Yang Fang, Tim Scheibe, et al.. (2005). Calcium carbonate precipitation along solution-solution interfaces in porous media. AGU Fall Meeting Abstracts. 2005. 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.

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