Data-driven System Identification of Thermal Systems using Machine Learning
Created by W.Langdon from
gp-bibliography.bib Revision:1.7810
- @Article{NECHITA:2021:IFAC-PapersOnLine,
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author = "Stefan-Cristian Nechita and Roland Toth and
Koos {van Berkel}",
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title = "Data-driven System Identification of Thermal Systems
using Machine Learning",
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journal = "IFAC-PapersOnLine",
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volume = "54",
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number = "7",
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pages = "162--167",
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year = "2021",
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note = "19th IFAC Symposium on System Identification SYSID
2021",
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ISSN = "2405-8963",
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DOI = "
doi:10.1016/j.ifacol.2021.08.352",
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URL = "
https://www.sciencedirect.com/science/article/pii/S2405896321011265",
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keywords = "genetic algorithms, genetic programming,
Spatial-temporal System Identification, Separation of
Variables, Machine Learning, MIMO System
Indentification, Tree Adjoining Grammar, Equation
Discovery, Gaussian Proccesses",
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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