May 14, 2024, 4:42 a.m. | Paris Papavasileiou, Dimitrios G. Giovanis, Gabriele Pozzetti, Martin Kathrein, Christoph Czettl, Ioannis G. Kevrekidis, Andreas G. Boudouvis, St\'eph

cs.LG updates on

arXiv:2405.07751v1 Announce Type: new
Abstract: 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) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we address the pivotal question of the significance of each input in shaping the process outcome, using an industrial Chemical Vapor Deposition (CVD) process as an example. The initial objective …

abstract arxiv categorical cs.lg framework generate industrial inputs machine machine learning numerical parameters predictions process processes production quantitative sample scale study type unsupervised unsupervised learning

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