8 citations · 8 across the 4 of their papers we have counts for
4 papers
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…
Implementing NLPs in industrial process modeling: Addressing Categorical Variables
Eleni D. Koronaki, Geremy Loachamin Suntaxi, Paris Papavasileiou +4
Important variables of processes are often categorical, i.e. names or labels representing, e.g. categories of inputs, or types of reactors or a sequence of steps. In this work, we…
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…