Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations. - Dimitris G. Giovanis; Michael D. Shields (J. Comput. Phys., 2018)

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold. - Dimitris G. Giovanis; Michael D. Shields (CoRR, 2020)

UQpy: A general purpose Python package and development environment for uncertainty quantification. - Audrey Olivier; Dimitris G. Giovanis; B. S. Aakash; Mohit Singh Chauhan; Lohit Vandanapu; Michael D. Shields (J. Comput. Sci., 2020)

Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models. - Katiana Kontolati; Dimitrios Loukrezis; Ketson R. M. dos Santos; Dimitrios G. Giovanis; Michael D. Shields (CoRR, 2021)

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. - Katiana Kontolati; Dimitrios Loukrezis; Dimitrios G. Giovanis; Lohit Vandanapu; Michael D. Shields (J. Comput. Phys., 2022)

Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data. - Ketson R. M. dos Santos; Dimitrios G. Giovanis; Michael D. Shields (SIAM J. Sci. Comput., 2022)

Machine Learning for the identification of phase-transitions in interacting agent-based systems. - Nikolaos Evangelou; Dimitrios G. Giovanis; George A. Kevrekidis; Grigorios A. Pavliotis; Ioannis G. Kevrekidis (CoRR, 2023)

UQpy v4.1: Uncertainty quantification with Python. - Dimitrios Tsapetis; Michael D. Shields; Dimitris G. Giovanis; Audrey Olivier; Lukás Novák; Promit Chakroborty; Himanshu Sharma; Mohit Singh Chauhan; Katiana Kontolati; Lohit Vandanapu; Dimitrios Loukrezis; Michael Gardner (SoftwareX, 2023)

Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis. - 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 (CoRR, 2024)

Implementing LLMs in industrial process modeling: Addressing Categorical Variables. - Eleni D. Koronaki; Geremy Loachamín Suntaxi; Paris Papavasileiou; Dimitrios G. Giovanis; Martin Kathrein; Andreas G. Boudouvis; Stéphane P. A. Bordas (CoRR, 2024)

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification. - Dimitris G. Giovanis; Dimitrios Loukrezis; Ioannis G. Kevrekidis; Michael D. Shields (J. Comput. Phys., 2024)

Implementing NLP in industrial process modeling: Addressing categorical variables. - Eleni D. Koronaki; Geremy Loachamín Suntaxi; Paris Papavasileiou; Dimitrios G. Giovanis; Martin Kathrein; Christoph Czettl; Andreas G. Boudouvis; Stéphane P. A. Bordas (Comput. Chem. Eng., 2025)

Integrating supervised and unsupervised learning approaches to unveil critical process inputs. - Paris Papavasileiou; Dimitrios G. Giovanis; Gabriele Pozzetti; Martin Kathrein; Christoph Czettl; Ioannis G. Kevrekidis; Andreas G. Boudouvis; Stéphane P. A. Bordas; Eleni D. Koronaki (Comput. Chem. Eng., 2025)

Neural Operators for Stochastic Modeling of Nonlinear Structural System Response to Natural Hazards. - Somdatta Goswami; Dimitris G. Giovanis; Bowei Li; Seymour M. J. Spence; Michael D. Shields (CoRR, 2025)

Generative Learning of Densities on Manifolds. - Dimitris G. Giovanis; Ellis R. Crabtree; Roger G. Ghanem; Ioannis G. Kevrekidis (CoRR, 2025)

Generative Learning for Slow Manifolds and Bifurcation Diagrams. - Ellis R. Crabtree; Dimitris G. Giovanis; Nikolaos Evangelou; Juan M. Bello-Rivas; Ioannis G. Kevrekidis (CoRR, 2025)

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps. - Dimitris G. Giovanis; Nikolaos Evangelou; Ioannis G. Kevrekidis; Roger G. Ghanem (CoRR, 2025)