Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Koronaki, Eleni D."'
Autor:
Koronaki, Eleni D., Suntaxi, Geremy Loachamin, Papavasileiou, Paris, Giovanis, Dimitrios G., Kathrein, Martin, Boudouvis, Andreas G., Bordas, Stéphane P. A.
Important variables of processes are, in many occasions, 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 use Large Language Models (LLMs) to derive embeddings of
Externí odkaz:
http://arxiv.org/abs/2409.19097
For multiple scientific endeavors it is common to measure a phenomenon of interest in more than one ways. We make observations of objects from several different perspectives in space, at different points in time; we may also measure different propert
Externí odkaz:
http://arxiv.org/abs/2408.15344
Autor:
Sroczynski, David W., Dietrich, Felix, Koronaki, Eleni D., Talmon, Ronen, Coifman, Ronald R., Bollt, Erik, Kevrekidis, Ioannis G.
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 going to be. Here we demonstrate two distinct, data-driven ways of in
Externí odkaz:
http://arxiv.org/abs/2406.06812
Autor:
Suntaxi, Geremy Loachamín, Papavasileiou, Paris, Koronaki, Eleni D., Giovanis, Dimitrios G., Gakis, Georgios, Aviziotis, Ioannis G., Kathrein, Martin, Pozzetti, Gabriele, Czettl, Christoph, Bordas, Stéphane P. A., Boudouvis, Andreas G.
This work introduces a comprehensive approach utilizing data-driven methods to elucidate the deposition process regimes in Chemical Vapor Deposition (CVD) reactors and the interplay of physical mechanism that dominate in each one of them. Through thi
Externí odkaz:
http://arxiv.org/abs/2405.18444
Autor:
Papavasileiou, Paris, Giovanis, Dimitrios G., Pozzetti, Gabriele, Kathrein, Martin, Czettl, Christoph, Kevrekidis, Ioannis G., Boudouvis, Andreas G., Bordas, Stéphane P. A., Koronaki, Eleni D.
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 to (i) discern critical parameters influencing the output and (ii) gen
Externí odkaz:
http://arxiv.org/abs/2405.07751
Autor:
Koronaki, Eleni D., Kaven, Luise F., Faust, Johannes M. M., Kevrekidis, Ioannis G., Mitsos, Alexander
Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in-line concentration monitoring. Recent approaches and some theoreti
Externí odkaz:
http://arxiv.org/abs/2403.08376
Autor:
Koronaki, Eleni D., Evangelou, Nikolaos, Martin-Linares, Cristina P., Titi, Edriss S., Kevrekidis, Ioannis G.
This study presents a collection of purely data-driven workflows for constructing reduced-order models (ROMs) for distributed dynamical systems. The ROMs we focus on, are data-assisted models inspired by, and templated upon, the theory of Approximate
Externí odkaz:
http://arxiv.org/abs/2310.15816
Autor:
Martin-Linares, Cristina P., Psarellis, Yorgos M., Karapetsas, Georgios, Koronaki, Eleni D., Kevrekidis, Ioannis G.
Numerical simulations of multiphase flows are crucial in numerous engineering applications, but are often limited by the computationally demanding solution of the Navier-Stokes (NS) equations. Here, we present a data-driven workflow where a handful o
Externí odkaz:
http://arxiv.org/abs/2301.12508
Autor:
Koronaki, Eleni D., Evangelou, Nikolaos, Psarellis, Yorgos M., Boudouvis, Andreas G., Kevrekidis, Ioannis G.
Publikováno v:
Computers & Chemical Engineering, Volume 178, 2023
A data-driven framework is presented, that enables the prediction of quantities, either observations or parameters, given sufficient partial data. The framework is illustrated via a computational model of the deposition of Cu in a Chemical Vapor Depo
Externí odkaz:
http://arxiv.org/abs/2301.11728
Autor:
Loachamín-Suntaxi, Geremy, Papavasileiou, Paris, Koronaki, Eleni D., Giovanis, Dimitrios G., Gakis, Georgios, Aviziotis, Ioannis G., Kathrein, Martin, Pozzetti, Gabriele, Czettl, Christoph, Bordas, Stéphane P.A., Boudouvis, Andreas G.
Publikováno v:
In Chemical Engineering Journal Advances 15 November 2024 20
