Dimitris G. Giovanis

1.2k total citations
34 papers, 819 citations indexed

About

Dimitris G. Giovanis is a scholar working on Statistics, Probability and Uncertainty, Civil and Structural Engineering and Statistical and Nonlinear Physics. According to data from OpenAlex, Dimitris G. Giovanis has authored 34 papers receiving a total of 819 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Statistics, Probability and Uncertainty, 11 papers in Civil and Structural Engineering and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in Dimitris G. Giovanis's work include Probabilistic and Robust Engineering Design (21 papers), Structural Health Monitoring Techniques (10 papers) and Model Reduction and Neural Networks (6 papers). Dimitris G. Giovanis is often cited by papers focused on Probabilistic and Robust Engineering Design (21 papers), Structural Health Monitoring Techniques (10 papers) and Model Reduction and Neural Networks (6 papers). Dimitris G. Giovanis collaborates with scholars based in United States, Greece and Germany. Dimitris G. Giovanis's co-authors include Vissarion Papadopoulos, Manolis Papadrakakis, Michael D. Shields, Nikos D. Lagaros, Jinbo Bai, Xiaoxin Lu, Fabrice Detrez, Julien Yvonnet, Dániel Straub and Iason Papaioannou and has published in prestigious journals such as Nature Communications, Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering.

In The Last Decade

Dimitris G. Giovanis

29 papers receiving 796 citations

Peers

Dimitris G. Giovanis
Chunlin Gong China
Anirban Basudhar United States
Bingyu Ni China
Wenliang Fan China
Wolfgang Betz Germany
Diego A. Álvarez Colombia
Dimos C. Charmpis Cyprus
Alba Sofi Italy
Thomas L. Paez United States
Steffen Freitag Germany
Chunlin Gong China
Dimitris G. Giovanis
Citations per year, relative to Dimitris G. Giovanis Dimitris G. Giovanis (= 1×) peers Chunlin Gong

Countries citing papers authored by Dimitris G. Giovanis

Since Specialization
Citations

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

Fields of papers citing papers by Dimitris G. Giovanis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dimitris G. Giovanis

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

All Works

20 of 20 papers shown
1.
Koronaki, Eleni D., Dimitris G. Giovanis, M. Kathrein, et al.. (2025). Implementing NLP in industrial process modeling: Addressing categorical variables. Computers & Chemical Engineering. 199. 109146–109146.
2.
Giovanis, Dimitris G., et al.. (2025). Generative learning of densities on manifolds. Computer Methods in Applied Mechanics and Engineering. 446. 118266–118266.
3.
Goswami, Somdatta, et al.. (2025). Neural operators for stochastic modeling of nonlinear structural system response to natural hazards. Engineering Structures. 345. 121284–121284.
4.
Chandross, Michael, Rahul Kumar Meena, Dimitris G. Giovanis, et al.. (2024). Revealing the hidden structure of disordered materials by parameterizing their local structural manifold. Nature Communications. 15(1). 4424–4424. 5 indexed citations
5.
Giovanis, Dimitris G., et al.. (2024). Machine learning for the identification of phase transitions in interacting agent-based systems: A Desai-Zwanzig example. Physical review. E. 110(1). 14121–14121.
6.
Koronaki, Eleni D., Dimitris G. Giovanis, Georgios P. Gakis, et al.. (2024). Discovering deposition process regimes: Leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis. Chemical Engineering Journal Advances. 20. 100667–100667.
7.
Giovanis, Dimitris G., et al.. (2024). Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification. Journal of Computational Physics. 519. 113443–113443. 3 indexed citations
8.
Upadhyay, Kshitiz, Dimitris G. Giovanis, Ahmed Alshareef, et al.. (2024). Effect of Human Head Shape on the Risk of Traumatic Brain Injury: A Gaussian Process Regression-Based Machine Learning Approach. Military Medicine. 189(Supplement_3). 608–617. 2 indexed citations
9.
Shields, Michael D., et al.. (2023). UQpy v4.1: Uncertainty quantification with Python. SoftwareX. 24. 101561–101561. 10 indexed citations
10.
Giovanis, Dimitris G., et al.. (2022). A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. arXiv (Cornell University). 32 indexed citations
11.
Upadhyay, Kshitiz, Dimitris G. Giovanis, Ahmed Alshareef, et al.. (2022). Data-driven uncertainty quantification in computational human head models. Computer Methods in Applied Mechanics and Engineering. 398. 115108–115108. 14 indexed citations
12.
Santos, Ketson R.M. dos, et al.. (2022). MANIFOLD LEARNING-BASED POLYNOMIAL CHAOS EXPANSIONS FOR HIGH-DIMENSIONAL SURROGATE MODELS. International Journal for Uncertainty Quantification. 12(4). 39–64. 29 indexed citations
13.
Giovanis, Dimitris G., et al.. (2022). A Survey of Unsupervised Learning Methods for High-Dimensional Uncertainty Quantification in Black-Box-Type Problems. SSRN Electronic Journal. 2 indexed citations
14.
Giovanis, Dimitris G. & Michael D. Shields. (2020). Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold. Computer Methods in Applied Mechanics and Engineering. 370. 113269–113269. 42 indexed citations
15.
Giovanis, Dimitris G., et al.. (2017). A GPU domain decomposition solution for spectral stochastic finite element method. Computer Methods in Applied Mechanics and Engineering. 327. 392–410. 15 indexed citations
16.
Papadopoulos, Vissarion & Dimitris G. Giovanis. (2017). Stochastic Finite Element Methods. 22 indexed citations
17.
Giovanis, Dimitris G., Michalis Fragiadakis, & Vissarion Papadopoulos. (2015). Epistemic uncertainty assessment using Incremental Dynamic Analysis and Neural Networks. Bulletin of Earthquake Engineering. 14(2). 529–547. 30 indexed citations
18.
Giovanis, Dimitris G., et al.. (2015). An adaptive spectral Galerkin stochastic finite element method using variability response functions. International Journal for Numerical Methods in Engineering. 104(3). 185–208. 1 indexed citations
19.
Giovanis, Dimitris G. & Vissarion Papadopoulos. (2014). Spectral representation-based neural network assisted stochastic structural mechanics. Engineering Structures. 84. 382–394. 26 indexed citations
20.
Papadopoulos, Vissarion, Dimitris G. Giovanis, Nikos D. Lagaros, & Manolis Papadrakakis. (2012). Accelerated subset simulation with neural networks for reliability analysis. Computer Methods in Applied Mechanics and Engineering. 223-224. 70–80. 176 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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