Mathematical and Computational Applications, Volume 28, Issue 5
2023 October - 12 articles
Cover Story: Recently, Physics-Informed Neural Networks (PINNs) have drawn attention for solving computational physics issues. Unlike traditional neural networks that rely heavily on a large amount of labeled data, PINNs incorporate physical laws into the neural network's loss function. This method ensures output variables comply with physical equations, eliminating the need for labeled data. This research utilizes PINNs to solve the classic Navier–Stokes equations in thermal fluid engineering, focusing on a 2D incompressible laminar flow. We examine flows around a circular and elliptical particle. Additionally, the particle drag force coefficient is numerically computed to quantify the discrepancy in the results of the PINNs as compared to CFD outcomes. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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