Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Giovanis, Dimitrios G."'
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
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:
Evangelou, Nikolaos, Giovanis, Dimitrios G., Kevrekidis, George A., Pavliotis, Grigorios A., Kevrekidis, Ioannis G.
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 phase- and noise-induced transitions for agent-based models (ABMs). In thi
Externí odkaz:
http://arxiv.org/abs/2310.19039
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
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.
Publikováno v:
In Computers and Chemical Engineering January 2025 192
Autor:
Santos, Ketson R. M. dos, Giovanis, Dimitrios G., Kontolati, Katiana, Loukrezis, Dimitrios, Shields, Michael D.
In this paper, a novel surrogate model based on the Grassmannian diffusion maps (GDMaps) and utilizing geometric harmonics is developed for predicting the response of engineering systems and complex physical phenomena. The method utilizes the GDMaps
Externí odkaz:
http://arxiv.org/abs/2109.13805
Autor:
Kontolati, Katiana, Loukrezis, Dimitrios, Santos, Ketson R. M. dos, Giovanis, Dimitrios G., Shields, Michael D.
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 identify the embedding of a set of high-dimensional data representing qua
Externí odkaz:
http://arxiv.org/abs/2107.09814
Autor:
Kontolati, Katiana, Loukrezis, Dimitrios, Giovanis, Dimitrios G., Vandanapu, Lohit, Shields, Michael D.
Publikováno v:
In Journal of Computational Physics 1 September 2022 464
Publikováno v:
In Probabilistic Engineering Mechanics July 2022 69