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Keywords: PINNs
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Proceedings Papers

Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A014, August 3–5, 2022
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2022-86953
... Abstract Physics Informed Neural Networks (PINNs) incorporate known physics equations into a network to reduce training time and increase accuracy. Traditional PINNs approaches are based on dense networks that do not consider the fact that simulations are a type of sequential data. Long-Short...
Proceedings Papers

Proc. ASME. FEDSM2022, Volume 2: Multiphase Flow (MFTC); Computational Fluid Dynamics (CFDTC); Micro and Nano Fluid Dynamics (MNFDTC), V002T05A015, August 3–5, 2022
Publisher: American Society of Mechanical Engineers
Paper No: FEDSM2022-86957
... Abstract Physics Informed Neural Networks (PINNs) provide a way to apply deep learning to train a network using data and governing differential equations that control the physical behavior of a system. In this text, we propose using the PINNs framework to solve an inverse problem which...