Journal
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
Volume 373, Issue -, Pages -Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2020.113452
Keywords
Deep learning; Machine learning; Reduced order model; Data-driven discovery; Multiscale simulation; Artificial intelligence
Funding
- National Science Foundation (NSF, USA) [CMMI-1762035, CMMI-1934367]
- AFOSR, USA [FA9550-18-1-0381]
- EPSRC [EP/J014389/1, EP/M002322/2, EP/M002322/1] Funding Source: UKRI
Ask authors/readers for more resources
The unified AI-framework HiDeNN is proposed for solving challenging computational science and engineering problems, with demonstrated accuracy, efficiency, and versatility in three example problems. The framework shows potential for advanced engineering problems that require state-of-the-art AI approaches.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available