Physics-Informed Neural Networks for Heat Transfer and Fluid Flow problems

Date

2025-05-21

Author

Aygün, Atakan
Ata, Onur
Karakuş, Ali

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Physics-informed neural networks (PINN) have emerged as a viable alternative to the classicalnumerical methods while solving partial differential equations. Their ability to obtain the solutionwithout generating any mesh or using a discretization method without any labeled data have drawnattention in recent years. PINNs struggle to obtain the correct solution when the problem has multiscalebehavior or highly nonlinear behavior. In this talk, we will show the solutions for thermal convectionproblems in different flow regimes. We will show the results for different thermal convection problemsand show the applicability of PINNs on heat transfer and fluid flow problems.

URI

https://hdl.handle.net/11511/114809

Conference Name

15th International Conference on Computational Heat and Mass Transfer

Collections

Department of Mechanical Engineering, Conference / Seminar

Citation Formats

A. Aygün, O. Ata, and A. Karakuş, “Physics-Informed Neural Networks for Heat Transfer and Fluid Flow problems,” presented at the 15th International Conference on Computational Heat and Mass Transfer, Antalya, Türkiye, 2025, Accessed: 00, 2025. [Online]. Available: https://hdl.handle.net/11511/114809.