Maziar Raissi

33 papers and 11.7k indexed citations i.

About

Maziar Raissi is a scholar working on Statistical and Nonlinear Physics, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Maziar Raissi has authored 33 papers receiving a total of 11.7k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Statistical and Nonlinear Physics, 12 papers in Artificial Intelligence and 7 papers in Computational Mechanics. Recurrent topics in Maziar Raissi’s work include Model Reduction and Neural Networks (23 papers), Gaussian Processes and Bayesian Inference (8 papers) and Fluid Dynamics and Turbulent Flows (3 papers). Maziar Raissi is often cited by papers focused on Model Reduction and Neural Networks (23 papers), Gaussian Processes and Bayesian Inference (8 papers) and Fluid Dynamics and Turbulent Flows (3 papers). Maziar Raissi collaborates with scholars based in United States, United Kingdom and Portugal. Maziar Raissi's co-authors include George Em Karniadakis, Paris Perdikaris, Alireza Yazdani, Vincenzo Schiano Di Cola, Francesco Piccialli, Salvatore Cuomo, Gianluigi Rozza, Fabio Giampaolo, Adrian Moure and Héctor Gómez and has published in prestigious journals such as Science, Journal of Fluid Mechanics and Journal of Computational Physics.

In The Last Decade

Co-authorship network of co-authors of Maziar Raissi i

Fields of papers citing papers by Maziar Raissi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Maziar Raissi

Since Specialization
Citations

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

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

Rankless by CCL
2025