ModalPINN: An extension of physics-informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors

Gaetan Raynaud, Sébastien Houde and Frederick Gosselin

Article (2022)

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Department:	Department of Mechanical Engineering
Research Center:	LM2 - Laboratory for Multi-scale Mechanics
PolyPublie URL:	https://publications.polymtl.ca/50915/
Journal Title:	Journal of Computational Physics (vol. 464)
Publisher:	Academic Press Inc.
DOI:	10.1016/j.jcp.2022.111271
Official URL:	https://doi.org/10.1016/j.jcp.2022.111271
Date Deposited:	18 Apr 2023 14:59
Last Modified:	08 Apr 2025 07:18

Cite in APA 7:	Raynaud, G., Houde, S., & Gosselin, F. (2022). ModalPINN: An extension of physics-informed Neural Networks with enforced truncated Fourier decomposition for periodic flow reconstruction using a limited number of imperfect sensors. Journal of Computational Physics, 464, 18 pages. https://doi.org/10.1016/j.jcp.2022.111271
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