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ORCIDDimitris G. Giovanis
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Journal Articles
- 2025
[j7]Paris Papavasileiou
, Dimitrios G. Giovanis, Gabriele Pozzetti, Martin Kathrein, Christoph Czettl, Ioannis G. Kevrekidis, Andreas G. Boudouvis, Stéphane P. A. Bordas, Eleni D. Koronaki:
Integrating supervised and unsupervised learning approaches to unveil critical process inputs. Comput. Chem. Eng. 192: 108857 (2025)- 2024
- [j6]Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis, Michael D. Shields:
Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification. J. Comput. Phys. 519: 113443 (2024) - 2023
- [j5]Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukás Novák, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner:
UQpy v4.1: Uncertainty quantification with Python. SoftwareX 24: 101561 (2023) - 2022
- [j4]Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields:
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. J. Comput. Phys. 464: 111313 (2022) - [j3]Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields:
Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data. SIAM J. Sci. Comput. 44(2): 250- (2022) - 2020
- [j2]Audrey Olivier, Dimitris G. Giovanis, B. S. Aakash, Mohit Chauhan, Lohit Vandanapu, Michael D. Shields:
UQpy: A general purpose Python package and development environment for uncertainty quantification. J. Comput. Sci. 47: 101204 (2020) - 2018
- [j1]Dimitris G. Giovanis, Michael D. Shields:
Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations. J. Comput. Phys. 364: 393-415 (2018)
Informal and Other Publications
- 2024
- [i10]Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis, Michael D. Shields:
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification. CoRR abs/2401.16683 (2024) - [i9]Paris Papavasileiou, Dimitrios G. Giovanis, Gabriele Pozzetti, Martin Kathrein, Christoph Czettl, Ioannis G. Kevrekidis, Andreas G. Boudouvis, Stéphane P. A. Bordas, Eleni D. Koronaki:
Integrating supervised and unsupervised learning approaches to unveil critical process inputs. CoRR abs/2405.07751 (2024) - [i8]Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki, Dimitrios G. Giovanis, Georgios Gakis, Ioannis G. Aviziotis, Martin Kathrein, Gabriele Pozzetti, Christoph Czettl, Stéphane P. A. Bordas, Andreas G. Boudouvis:
Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis. CoRR abs/2405.18444 (2024) - [i7]Eleni D. Koronaki, Geremy Loachamín Suntaxi, Paris Papavasileiou, Dimitrios G. Giovanis, Martin Kathrein, Andreas G. Boudouvis, Stéphane P. A. Bordas:
Implementing LLMs in industrial process modeling: Addressing Categorical Variables. CoRR abs/2409.19097 (2024) - 2023
- [i6]Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukás Novák, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner:
UQpy v4.1: Uncertainty Quantification with Python. CoRR abs/2305.09572 (2023) - [i5]Nikolaos Evangelou, Dimitrios G. Giovanis, George A. Kevrekidis, Grigorios A. Pavliotis, Ioannis G. Kevrekidis:
Machine Learning for the identification of phase-transitions in interacting agent-based systems. CoRR abs/2310.19039 (2023) - 2022
- [i4]Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields:
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. CoRR abs/2202.04648 (2022) - 2021
- [i3]Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields:
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models. CoRR abs/2107.09814 (2021) - 2020
- [i2]Dimitris G. Giovanis, Michael D. Shields:
Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold. CoRR abs/2003.11910 (2020) - [i1]K. R. M. dos Santos, Dimitris G. Giovanis, Michael D. Shields:
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data. CoRR abs/2009.07547 (2020)
Coauthor Index
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