39 citations · 48 across the 7 of their papers we have counts for
7 papers
Learning to Choose: An Empowerment-Guided Multi-Agent System with semantic communication for Adaptive Method Selection
Geremy Loachamín-Suntaxi, Robert Lazar, Dimitrios G. Giovanis +2
Automating scientific computing workflows requires more than generating executable code: autonomous systems must also select appropriate computational strategies, implement them fa…
Data-driven inverse uncertainty quantification: application to the Chemical Vapor Deposition Reactor Modeling
Geremy Loachamín, Eleni D. Koronaki, Dimitrios G. Giovanis +4
This study presents a Bayesian framework for (inverse) uncertainty quantification and parameter estimation in a two-step Chemical Vapor Deposition coating process using production…
Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis
Geremy Loachamín Suntaxi, Paris Papavasileiou, Eleni D. Koronaki +8
This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interpla…
Integrating supervised and unsupervised learning approaches to unveil critical process inputs
Paris Papavasileiou, Dimitrios G. Giovanis, Gabriele Pozzetti +6
This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims…
Machine Learning for the identification of phase-transitions in interacting agent-based systems: a Desai-Zwanzig example
Nikolaos Evangelou, Dimitrios G. Giovanis, George A. Kevrekidis +2
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 phas…
Imprecise Subset Simulation
Dimitris G. Giovanis, Michael Shields
The objective of this work is to quantify the uncertainty in probability of failure estimates resulting from incomplete knowledge of the probability distributions for the input ran…
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos +2
In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to ide…