Výsledky vyhledávání - "Czettl, Christoph"

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Discovering deposition process regimes: leveraging unsupervised learning for process insights, surrogate modeling, and sensitivity analysis

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

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Integrating supervised and unsupervised learning approaches to unveil critical process inputs

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

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FORC Diagram Features of Co Particles due to Reversal by Domain Nucleation

Autor: Breth, Leoni, Fischbacher, Johann, Kovacs, Alexander, Özelt, Harald, Schrefl, Thomas, Brückl, Hubert, Czettl, Christoph, Kührer, Saskia, Pachlhofer, Julia, Schwarz, Maria

First Order Reversal Curve (FORC) diagrams are a popular tool in geophysics and materials science for the characterization of magnetic particles of natural and synthetic origin. However, there is still a lot of controversy about the rigorous interpre

Externí odkaz: http://arxiv.org/abs/2207.11011

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