Dimitris G. Giovanis

According to our database, Dimitris G. Giovanis authored at least 9 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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

CoRR, 2024

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 Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner

SoftwareX, December, 2023

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

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

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

2021

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

2020

UQpy: A general purpose Python package and development environment for uncertainty quantification.

Audrey Olivier, Dimitris G. Giovanis, B. S. Aakash, Mohit Chauhan, Lohit Vandanapu, Michael D. Shields

J. Comput. Sci., 2020

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold.

Dimitris G. Giovanis, Michael D. Shields

CoRR, 2020

2018

Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations.

Dimitris G. Giovanis, Michael D. Shields

J. Comput. Phys., 2018