论文标题
迈向机器学习管道，以减少逆问题的顺序建模：边界参数化，降低降低和解决方案歧管近似的神经网络
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation
论文作者
论文摘要
在这项工作中，我们提出了一个模型订单减少框架，以在非侵入性环境中处理反问题。逆问题，尤其是在部分微分方程上下文中，由于迭代优化过程，需要巨大的计算负载。为了加速此类过程，我们应用了涉及人造神经网络的数值管道来参数手头问题的边界条件，压缩（全阶）快照的维度，并近似参数解决方案歧管。它得出了一个通用框架，能够提供入口边界的临时参数化，并得益于模型订单降低，并迅速收敛到最佳解决方案。我们在此贡献中介绍了通过将这种方法应用于两个不同CFD测试用例获得的结果。
In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting. Inverse problems, especially in a partial differential equation context, require a huge computational load due to the iterative optimization process. To accelerate such a procedure, we apply a numerical pipeline that involves artificial neural networks to parametrize the boundary conditions of the problem in hand, compress the dimensionality of the (full-order) snapshots, and approximate the parametric solution manifold. It derives a general framework capable to provide an ad-hoc parametrization of the inlet boundary and quickly converges to the optimal solution thanks to model order reduction. We present in this contribution the results obtained by applying such methods to two different CFD test cases.
