论文标题
仿真免费可靠性分析：一种基于物理学的深度学习方法
Simulation free reliability analysis: A physics-informed deep learning based approach
论文作者
论文摘要
本文介绍了一个免费的框架，用于解决可靠性分析问题。所提出的方法植根于最近开发的深度学习方法，称为物理知识的神经网络。主要思想是直接从问题的物理学中学习神经网络参数。这样，完全消除了运行模拟和生成数据的需求。此外，拟议的方法还满足了与问题相关的不变性属性和保护定律等物理定律。所提出的方法用于解决三个基准可靠性分析问题。获得的结果表明，所提出的方法是高度准确的。此外，使用这种方法消除了解决可靠性分析问题的主要瓶颈，即运行昂贵的模拟来生成数据。
This paper presents a simulation free framework for solving reliability analysis problems. The method proposed is rooted in a recently developed deep learning approach, referred to as the physics-informed neural network. The primary idea is to learn the neural network parameters directly from the physics of the problem. With this, the need for running simulation and generating data is completely eliminated. Additionally, the proposed approach also satisfies physical laws such as invariance properties and conservation laws associated with the problem. The proposed approach is used for solving three benchmark reliability analysis problems. Results obtained illustrates that the proposed approach is highly accurate. Moreover, the primary bottleneck of solving reliability analysis problems, i.e., running expensive simulations to generate data, is eliminated with this method.
