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MPIPN: a multi physics-informed PointNet for solving parametric acoustic-structure systems

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Abstract

Machine learning is employed for solving physical systems governed by general nonlinear partial differential equations (PDEs). However, complex multi-physics systems such as acoustic-structure coupling are often described by a series of PDEs that incorporate variable physical quantities, which are referred to as parametric systems. There are lack of strategies for solving parametric systems governed by PDEs that involve explicit and implicit quantities. In this paper, a deep learning-based Multi Physics-Informed PointNet (MPIPN) is proposed for solving parametric acoustic-structure systems. First, the MPIPN introduces an enhanced point-cloud architecture that encompasses explicit physical quantities and geometric features of computational domains. Then, the MPIPN extracts local and global features of the reconstructed point-cloud as parts of solving criteria of parametric systems, respectively. Besides, implicit physical quantities are embedded by encoding techniques as another part of solving criteria. Finally, all solving criteria that characterize parametric systems are amalgamated to form distinctive sequences as the input of the MPIPN, whose outputs are solutions of systems. The proposed framework is trained by adaptive physics-informed loss functions for corresponding computational domains. The framework is generalized to deal with new parametric conditions of systems. The effectiveness of the MPIPN is validated by applying it to solve steady parametric acoustic-structure coupling systems governed by the Helmholtz equations. An ablation experiment has been implemented to demonstrate the efficacy of physics-informed impact with a minority of supervised data. The proposed method yields reasonable precision across all computational domains under constant parametric conditions and changeable combinations of parametric conditions for acoustic-structure systems.

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The data will be available if the corresponding author receives any appropriate request. We are pleased and delighted to offer any help and access to the data we use in this research.

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Acknowledgements

This research has been partially supported by the National Natural Science Foundation of China (NSFC) under Grant no. 52175231.

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Authors and Affiliations

  1. School of Aerospace Engineering, Huazhong University of Science & Technology, Wuhan, 430074, Hubei, China

    Chu Wang, Jinhong Wu, Yanzhi Wang, Zhijian Zha & Qi Zhou

Authors
  1. Chu Wang
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  2. Jinhong Wu
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  3. Yanzhi Wang
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  4. Zhijian Zha
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  5. Qi Zhou
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Contributions

Chu Wang: Conceptualization, Data curation, Methodology, Software, Writing – original draft. Jinhong Wu: Investigation, Writing – review & editing. Yanzhi Wang: Investigation, Validation. Zhijian Zha: Validation. Qi Zhou: Funding acquisition, Writing – review & editing.

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Correspondence to Qi Zhou.

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Wang, C., Wu, J., Wang, Y. et al. MPIPN: a multi physics-informed PointNet for solving parametric acoustic-structure systems. Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-01998-w

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