Inverse flow prediction using ensemble PINNs and uncertainty quantification

The thermal boundary conditions in a numerical simulation for heat transfer are often imprecise. This leads to poorly defined boundary conditions for the energy equation. The lack of accurate thermal boundary conditions in real-world applications makes it impossible to effectively solve the problem, regardless of the advancement of conventional numerical methods. This study utilises a physics-informed neural network to tackle ill-posed problems for unknown thermal boundaries with limited sensor data. The network approximates velocity and temperature fields while complying with the Navier-Stokes and energy equations, thereby revealing unknown thermal boundaries and reconstructing the flow field around a square cylinder. The method relies on optimal sensor placement determined by the QR pivoting technique, which ensures the effective capture of the dynamics, leading to enhanced model accuracy. In an effort to increase the robustness and generalisability, an ensemble physics-informed neural network is implemented. This approach mitigates the risks of overfitting and underfitting while providing a measure of model confidence. As a result, the ensemble model can identify regions of reliable prediction and potential inaccuracies. Therefore, broadening its applicability in tackling complex heat transfer problems with unknown boundary conditions.

Aslanidou, IoannaMälardalens universitet,Innovation och produktrealisering(Swepub:mdh)iau01 (författare)
Kyprianidis, KonstantinosMälardalens universitet,Framtidens energi(Swepub:mdh)kks01 (författare)
Bel Fdhila, RebeiMälardalens universitet,Framtidens energi,Hitachi Energy Research, Västerås, Sweden.(Swepub:mdh)rba02 (författare)
Mälardalens universitetFramtidens energi (creator_code:org_t)

Av författaren/redakt...: Soibam, Jerol; Aslanidou, Ioann ...; Kyprianidis, Kon ...; Bel Fdhila, Rebe ...

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