no code implementations • 13 May 2024 • Paris Papavasileiou, Dimitrios G. Giovanis, Gabriele Pozzetti, Martin Kathrein, Christoph Czettl, Ioannis G. Kevrekidis, Andreas G. Boudouvis, Stéphane P. A. Bordas, Eleni D. Koronaki
This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs.
no code implementations • 29 Oct 2023 • Nikolaos Evangelou, Dimitrios G. Giovanis, George A. Kevrekidis, Grigorios A. Pavliotis, Ioannis G. Kevrekidis
Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs).
no code implementations • 28 Sep 2021 • Ketson R. M. dos Santos, Dimitrios G. Giovanis, Katiana Kontolati, Dimitrios Loukrezis, Michael D. Shields
Using this representation, geometric harmonics, an out-of-sample function extension technique, is employed to create a global map from the space of input parameters to a Grassmannian diffusion manifold.
2 code implementations • 21 Jul 2021 • Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos, Dimitrios G. Giovanis, Michael D. Shields
For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner.