Data-driven system identification of thermal systems using machine learning

Stefan Cristian Nechita; Roland Tóth; Koos van Berkel

doi:10.1016/j.ifacol.2021.08.352

Data-driven system identification of thermal systems using machine learning

Stefan Cristian Nechita
, Roland Tóth
, Koos van Berkel

Research output: Contribution to journal › Conference article › peer-review

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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.

Original language	English
Pages (from-to)	162-167
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	54
Issue number	7
DOIs	https://doi.org/10.1016/j.ifacol.2021.08.352
Publication status	Published - 1 Jul 2021
Event	19th IFAC Symposium on System Identification (SYSID 2021) - Virtual, Padova, Italy Duration: 13 Jul 2021 → 16 Jul 2021 Conference number: 19 https://www.sysid2021.org/

Bibliographical note

Funding Information:
This research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program and by the Dutch Organization for Scientific Research (NWO, domain TTW, grant: 13852) which is partly funded by the Ministry of Economic Affairs of The Netherlands.

Funding

This research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program and by the Dutch Organization for Scientific Research (NWO, domain TTW, grant: 13852) which is partly funded by the Ministry of Economic Affairs of The Netherlands.

Keywords

Equation Discovery
Gaussian Proccesses
Genetic Programming
Machine Learning
MIMO System Indentification
Separation of Variables
Spatial-temporal System Identification
Tree Adjoining Grammar

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10.1016/j.ifacol.2021.08.352

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Cite this

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title = "Data-driven system identification of thermal systems using machine learning",

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.",

keywords = "Equation Discovery, Gaussian Proccesses, Genetic Programming, Machine Learning, MIMO System Indentification, Separation of Variables, Spatial-temporal System Identification, Tree Adjoining Grammar",

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note = "Funding Information: This research was supported by the Ministry of Innovation and Technology NRDI Office within the framework of the Artificial Intelligence National Laboratory Program and by the Dutch Organization for Scientific Research (NWO, domain TTW, grant: 13852) which is partly funded by the Ministry of Economic Affairs of The Netherlands. ; 19th IFAC Symposium on System Identification (SYSID 2021), SYSID 2021 ; Conference date: 13-07-2021 Through 16-07-2021",

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KW - Equation Discovery

KW - Gaussian Proccesses

KW - Genetic Programming

KW - Machine Learning

KW - MIMO System Indentification

KW - Separation of Variables

KW - Spatial-temporal System Identification

KW - Tree Adjoining Grammar

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T2 - 19th IFAC Symposium on System Identification (SYSID 2021)

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ER -

Data-driven system identification of thermal systems using machine learning

Abstract

Bibliographical note

Funding

Keywords

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