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cs.LG2025
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…
cs.LG2024
On Learning what to Learn: heterogeneous observations of dynamics and establishing (possibly causal) relations among them
David W. Sroczynski, Felix Dietrich, Eleni D. Koronaki +4
Before we attempt to learn a function between two (sets of) observables of a physical process, we must first decide what the inputs and what the outputs of the desired function are…
cs.LG2024
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…