Physics informed neural networks (PINNs) have been explored extensively in the recent past for solving various forward and inverse problems for facilitating querying applications in fluid mechanics. However, investigations on PINNs for unsteady flows past moving bodies, such as flapping wings are scarce. Earlier studies mostly relied on transferring the problems to a body-attached frame of reference, which could be restrictive towards handling multiple moving bodies/deforming structures. The present study attempts to couple the benefits of PINNs with a fixed Eulerian frame of reference, and proposes an immersed boundary aware framework for developing surrogate models for unsteady flows past moving bodies. Specifically, high-resolution velocity reconstruction and pressure recovery as a hidden variable are the main goals. The framework has been developed by using downsampled velocity data obtained from prior simulations to train the PINNs model. The efficacy of the velocity reconstruction has been tested against high resolution IBM simulation data, whereas the efficacy of the pressure recovery has been tested against high resolution simulation data from an arbitrary Lagrange Eulerian (ALE) solver. Under the present framework, two PINN variants, (i) a moving-boundary-enabled standard Navier–Stokes based PINN (MB-PINN), and, (ii) a moving-boundary-enabled IBM based PINN (MB-IBM-PINN) have been formulated.

中文翻译：

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针对具有移动浸没边界的系统的基于物理的神经网络建模：应用于经过暴跌箔的不稳定流

近年来，物理学信息神经网络（PINN）已被广泛探索，用于解决各种正向和逆向问题，以促进流体力学中的查询应用。然而，对 PINN 流过运动物体（例如扑动的翅膀）的不稳定流的研究很少。早期的研究主要依赖于将问题转移到附着在身体上的参考系，这可能会限制处理多个移动体/变形结构。本研究试图将 PINN 的优点与固定的欧拉参考系结合起来，并提出了一种沉浸式边界感知框架，用于开发经过移动物体的不稳定流动的替代模型。具体来说，高分辨率速度重建和压力恢复作为隐藏变量是主要目标。该框架是通过使用先前模拟获得的下采样速度数据来训练 PINNs 模型而开发的。速度重建的功效已针对高分辨率 IBM 模拟数据进行了测试，而压力恢复的功效已针对来自任意拉格朗日欧拉 (ALE) 求解器的高分辨率模拟数据进行了测试。在当前框架下，有两个 PINN 变体，(i) 基于 Navier-Stokes 的支持移动边界的标准 PINN (MB-PINN)，以及 (ii) 基于 IBM 的支持移动边界的 PINN (MB-IBM-PINN) ）已制定。