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Data-driven discovery of the heat equation in an induction machine via sparse regression

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Abstract
Discovery of natural laws through input-output data analysis has been of considerable interest during the past decade. Various approach among which the increasingly growing body of sparsity-based algorithms have been recently proposed for the purpose of free-form system identification. There has however been limited discussion on the performance of these approaches when applied on experimental datasets. The aim of this paper is to study the capability of this technique for identifying the heat equation as the natural law of heat distribution from experimental data, obtained using a Totally-Enclosed-Fan-Cooled (TEFC) induction machine, with and without active cooling. The results confirm the usefulness of the algorithm as a method to identify the underlying natural law in a physical system in the form of a Partial Differential Equation (PDE).
Keywords
sparse regression, data-driven discovery, sparse group Lasso, heat equation, IDENTIFICATION, SELECTION

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MLA
Khatiry Goharoodi, Saeideh, et al. “Data-Driven Discovery of the Heat Equation in an Induction Machine via Sparse Regression.” 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2019, pp. 90–95.
APA
Khatiry Goharoodi, S., Nguyen Phuc, P., Dupré, L., & Crevecoeur, G. (2019). Data-driven discovery of the heat equation in an induction machine via sparse regression. In 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT) (pp. 90–95). Melbourne, AUSTRALIA.
Chicago author-date
Khatiry Goharoodi, Saeideh, Pieter Nguyen Phuc, Luc Dupré, and Guillaume Crevecoeur. 2019. “Data-Driven Discovery of the Heat Equation in an Induction Machine via Sparse Regression.” In 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 90–95.
Chicago author-date (all authors)
Khatiry Goharoodi, Saeideh, Pieter Nguyen Phuc, Luc Dupré, and Guillaume Crevecoeur. 2019. “Data-Driven Discovery of the Heat Equation in an Induction Machine via Sparse Regression.” In 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 90–95.
Vancouver
1.
Khatiry Goharoodi S, Nguyen Phuc P, Dupré L, Crevecoeur G. Data-driven discovery of the heat equation in an induction machine via sparse regression. In: 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT). 2019. p. 90–5.
IEEE
[1]
S. Khatiry Goharoodi, P. Nguyen Phuc, L. Dupré, and G. Crevecoeur, “Data-driven discovery of the heat equation in an induction machine via sparse regression,” in 2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), Melbourne, AUSTRALIA, 2019, pp. 90–95.
@inproceedings{8624013,
  abstract     = {Discovery of natural laws through input-output data analysis has been of considerable interest during the past decade. Various approach among which the increasingly growing body of sparsity-based algorithms have been recently proposed for the purpose of free-form system identification. There has however been limited discussion on the performance of these approaches when applied on experimental datasets. The aim of this paper is to study the capability of this technique for identifying the heat equation as the natural law of heat distribution from experimental data, obtained using a Totally-Enclosed-Fan-Cooled (TEFC) induction machine, with and without active cooling. The results confirm the usefulness of the algorithm as a method to identify the underlying natural law in a physical system in the form of a Partial Differential Equation (PDE).},
  author       = {Khatiry Goharoodi, Saeideh and Nguyen Phuc, Pieter and Dupré, Luc and Crevecoeur, Guillaume},
  booktitle    = {2019 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT)},
  isbn         = {9781538663769},
  issn         = {2643-2978},
  keywords     = {sparse regression,data-driven discovery,sparse group Lasso,heat equation,IDENTIFICATION,SELECTION},
  language     = {eng},
  location     = {Melbourne, AUSTRALIA},
  pages        = {90--95},
  title        = {Data-driven discovery of the heat equation in an induction machine via sparse regression},
  url          = {http://dx.doi.org/10.1109/icit.2019.8754983},
  year         = {2019},
}

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