Data-driven System Identification of Thermal Systems using Machine Learning

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{NECHITA:2021:IFAC-PapersOnLine,

• author = "Stefan-Cristian Nechita and Roland Toth and Koos {van Berkel}",
• title = "Data-driven System Identification of Thermal Systems using Machine Learning",
• journal = "IFAC-PapersOnLine",
• volume = "54",
• number = "7",
• pages = "162--167",
• year = "2021",
• note = "19th IFAC Symposium on System Identification SYSID 2021",
• ISSN = "2405-8963",
• DOI = "doi:10.1016/j.ifacol.2021.08.352",
• URL = "https://www.sciencedirect.com/science/article/pii/S2405896321011265",
• keywords = "genetic algorithms, genetic programming, Spatial-temporal System Identification, Separation of Variables, Machine Learning, MIMO System Indentification, Tree Adjoining Grammar, Equation Discovery, Gaussian Proccesses",
• abstract = "The paper addresses the identification of spatial-temporal mirror surface deformations as a result of laser-based heat load within the lithography process of integrated circuit production. The thermal diffusion and surface deformation are modeled by separation of the spatial-temporal effects using data-driven orthogonal decomposition. A novel tree adjoining grammar (TAG) and sparsity enhanced symbolic-regression-based learning methods are deployed to discover temporal dynamics that connect the spatial variation. The resulting data-driven procedure is applied to automatically synthetise a compact model representation of synthetic thermal effects induced mirror surface deformations",

}

Genetic Programming entries for Stefan-Cristian Nechita Roland Toth Koos van Berkel

Citations