Physics–guided neural networks for inversion–based feedforward control applied to hybrid stepper motors<sup>*</sup>

Fan, D.; Bolderman, M.; Koekebakker, S.; Butler, H.; Lazar, M.

doi:10.1109/ccta54093.2023.10252460

2023 IEEE Conference on Control Technology and Applications (CCTA) 2023

DOI: 10.1109/ccta54093.2023.10252460

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Physics–guided neural networks for inversion–based feedforward control applied to hybrid stepper motors^*

D. Fan,

M. Bolderman,

S. Koekebakker

et al.

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2024

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Physics Guided Neural Networks with Knowledge Graph

Gupta,

Siddique,

George

et al. 2024

Digital

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Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN.

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Physics Guided Neural Networks with Knowledge Graph

Gupta,

Siddique,

George

et al. 2024

Digital

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

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Physics–guided neural networks for inversion–based feedforward control applied to hybrid stepper motors^*

Cited by 1 publication

References 19 publications

Physics Guided Neural Networks with Knowledge Graph

Physics Guided Neural Networks with Knowledge Graph

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Physics–guided neural networks for inversion–based feedforward control applied to hybrid stepper motors*

Cited by 1 publication

References 19 publications

Physics Guided Neural Networks with Knowledge Graph

Physics Guided Neural Networks with Knowledge Graph

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Physics–guided neural networks for inversion–based feedforward control applied to hybrid stepper motors^*