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20172026
most citedData-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold

59 citations · 109 across the 17 of their papers we have counts for

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stat.ML2025

Enabling Probabilistic Learning on Manifolds through Double Diffusion Maps

Dimitris G Giovanis, Nikolaos Evangelou, Ioannis G Kevrekidis +1

We present a generative learning framework for probabilistic sampling based on an extension of the Probabilistic Learning on Manifolds (PLoM) approach, which is designed to generat…

stat.ML2024★ 5 cited

Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification

Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis +1

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform…