| Autor: |
Alhussein, Hussam, Daqaq, Mohammed |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
AIP Advances. 14 (2024) 045112 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1063/5.0197860 |
| Popis: |
Recent advances in the application of physics-informed learning into the field of fluid mechanics have been predominantly grounded in the Newtonian framework, primarly leveraging Navier-Stokes Equation or one of its various derivative to train a neural network. Here, we propose an alternative approach based on variational methods. The proposed approach uses the principle of minimum pressure gradient combined with the continuity constraint to train a neural network and predict the flow field in incompressible fluids. We describe the underlying principles of the proposed approach, then use a demonstrative example to illustrate its implementation and show that it reduces the computational time per training epoch when compared to the conventional approach. |
| Databáze: |
arXiv |
| Externí odkaz: |
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