Investigations on Physics-Informed Neural Networks for Aerodynamics
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914728390950912 |
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| author | Coulaud, Guillaume Le, Maxime Duvigneau, Régis |
| author_facet | Coulaud, Guillaume Le, Maxime Duvigneau, Régis |
| contents | Physics-Informed Neural Networks (PINNs) have recently emerged as a novel approach to simulate complex physical systems on the basis of both data observations and physical models. In this work, we investigate the use of PINNs for various applications in aerodynamics and we explain how to leverage their specific formulation to perform some tasks effectively. In particular, we demonstrate the ability of PINNs to construct parametric surrogate models, to achieve multiphysic couplings and to infer turbulence characteristics via data assimilation. The robustness and accuracy of the PINNs approach are analysed, then current issues and challenges are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17470 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Investigations on Physics-Informed Neural Networks for Aerodynamics Coulaud, Guillaume Le, Maxime Duvigneau, Régis Analysis of PDEs Optimization and Control Classical Physics Physics-Informed Neural Networks (PINNs) have recently emerged as a novel approach to simulate complex physical systems on the basis of both data observations and physical models. In this work, we investigate the use of PINNs for various applications in aerodynamics and we explain how to leverage their specific formulation to perform some tasks effectively. In particular, we demonstrate the ability of PINNs to construct parametric surrogate models, to achieve multiphysic couplings and to infer turbulence characteristics via data assimilation. The robustness and accuracy of the PINNs approach are analysed, then current issues and challenges are discussed. |
| title | Investigations on Physics-Informed Neural Networks for Aerodynamics |
| topic | Analysis of PDEs Optimization and Control Classical Physics |
| url | https://arxiv.org/abs/2403.17470 |