Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering
Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering
复制标题
分层深度学习神经网络 (HiDeNN)：计算科学与工程的人工智能 (AI) 框架
DOI：
10.1016/j.cma.2020.113452
复制
发表时间：
2021-01-01
影响因子：
7.2
通讯作者：
Liu, Wing Kam
中科院分区：
文献类型：
--
作者：
Saha, Sourav;Gan, Zhengtao;Liu, Wing Kam
在这项工作中，提出了一个名为分层深度学习神经网络（HiDeNN）的统一AI框架，用于解决具有挑战性的计算科学和工程问题，这些问题很少或没有可用的物理以及极端的计算需求。详细的建设和HiDeNN的数学元素进行了介绍和讨论，以显示不同领域的不同问题的框架的灵活性。三个例子的问题解决证明的准确性，效率和通用性的框架。第一个例子的设计表明，HiDeNN是能够实现更好的精度比传统的有限元方法，通过学习最佳节点位置和捕获的应力集中与粗网格。第二个例子应用HiDeNN进行多尺度分析，在宏观尺度的每个材料点使用子神经网络。最后一个例子演示了HiDeNN如何从实验数据中发现控制无量纲参数，以便使用减少的输入集来提高学习效率。我们进一步讨论和演示了需要最先进的人工智能方法的高级工程问题的解决方案，以及如何应用通用和灵活的系统（如HiDeNN-AI框架）来解决这些问题。(C)2020爱思唯尔B. V.保留所有权利。
In this work, a unified AI-framework named Hierarchical Deep Learning Neural Network (HiDeNN) is proposed to solve challenging computational science and engineering problems with little or no available physics as well as with extreme computational demand. The detailed construction and mathematical elements of HiDeNN are introduced and discussed to show the flexibility of the framework for diverse problems from disparate fields. Three example problems are solved to demonstrate the accuracy, efficiency, and versatility of the framework. The first example is designed to show that HiDeNN is capable of achieving better accuracy than conventional finite element method by learning the optimal nodal positions and capturing the stress concentration with a coarse mesh. The second example applies HiDeNN for multiscale analysis with sub-neural networks at each material point of macroscale. The final example demonstrates how HiDeNN can discover governing dimensionless parameters from experimental data so that a reduced set of input can be used to increase the learning efficiency. We further present a discussion and demonstration of the solution for advanced engineering problems that require state-of-the-art AI approaches and how a general and flexible system, such as HiDeNN-AI framework, can be applied to solve these problems. (C) 2020 Elsevier B.V. All rights reserved.