Data-driven system identification of thermal systems using machine learning
| Autor: | Roland Tóth, Ştefan-Cristian Nechita, Koos van Berkel |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Surface (mathematics)
Equation Discovery Computer science Separation of Variables System identification Genetic Programming Integrated circuit Thermal diffusivity Tree Adjoining Grammar law.invention Data-driven Tree-adjoining grammar Machine Learning Spatial-temporal System Identification Identification (information) Control and Systems Engineering law Gaussian Proccesses Thermal MIMO System Indentification Algorithm |
| Zdroj: | IFAC-PapersOnLine. 54(7):162-167 |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2021.08.352 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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