论文标题
基于神经网络启动的基于投影的模型订购降低，以减轻kolmogorov屏障至CFD模型的可降低性
Neural-Network-Augmented Projection-Based Model Order Reduction for Mitigating the Kolmogorov Barrier to Reducibility of CFD Models
论文作者
论文摘要
灵感来自于我们以前关于使用二次近似歧管缓解kolmogorov屏障的工作的启发，我们在本文中提出了一种可计算障碍的方法，用于结合基于投影的基于投影的减少阶模型（PROM）和人工神经网络（ANN），以减轻kolmogorov障碍，以减少回合供应流量流量问题的kolmogorov障碍。我们提出的主要目标是将解决方案的在线近似的维度降低到使用仿射和二次近似歧管的可能性。与以前用于构建非线性模型降低任意非线性歧管近似值的方法相反，它利用了ANN的一种或另一种形式，我们在本文中提出的Prom-ANN的训练并不涉及数据的尺寸与高维模型的尺寸缩放；而且，使用任何建立的超级还原方法，该prom-ann是可重新修复的。因此，与许多其他基于ANN的方法不同，我们在本文中提出的Prom-Ann概念对于大规模且与行业相关的CFD问题是实用的。此处证明了它的潜力，以解决参数，电击主导的基准问题。
Inspired by our previous work on mitigating the Kolmogorov barrier using a quadratic approximation manifold, we propose in this paper a computationally tractable approach for combining a projection-based reduced-order model (PROM) and an artificial neural network (ANN) for mitigating the Kolmogorov barrier to reducibility of convection-dominated flow problems. The main objective the PROM-ANN concept that we propose is to reduce the dimensionality of the online approximation of the solution beyond what is possible using affine and quadratic approximation manifolds. In contrast to previous approaches for constructing arbitrarily nonlinear manifold approximations for nonlinear model reduction that exploited one form or another of ANN, the training of the PROM-ANN we propose in this paper does not involve data whose dimension scales with that of the high-dimensional model; and this PROM-ANN is hyperreducible using any well-established hyperreduction method. Hence, unlike many other ANN-based approaches, the PROM-ANN concept we propose in this paper is practical for large-scale and industry-relevant CFD problems. Its potential is demonstrated here for a parametric, shock-dominated, benchmark problem.
