水准点(测量)
可靠性(半导体)
计算机科学
替代模型
不确定度量化
功能(生物学)
非线性系统
操作员(生物学)
人工神经网络
动力系统理论
高斯过程
随机过程
高斯分布
数学优化
机器学习
人工智能
算法
数学
统计
地理
化学
功率(物理)
抑制因子
物理
基因
生物
转录因子
进化生物学
量子力学
生物化学
大地测量学
作者
Shailesh Garg,Harshit Gupta,Souvik Chakraborty
标识
DOI:10.1016/j.engstruct.2022.114811
摘要
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates considerable computational time. We investigate the efficacy of recently proposed DeepONet in solving time dependent reliability analysis and uncertainty quantification of systems subjected to stochastic loading. Unlike conventional machine learning and deep learning algorithms, DeepONet is an operator network and learns a function to function mapping and hence, is ideally suited to propagate the uncertainty from the stochastic forcing function to the output responses. We use DeepONet to build a surrogate model for the dynamical system under consideration. Multiple case studies, involving both toy and benchmark problems, have been conducted to examine the efficacy of DeepONet in time dependent reliability analysis and uncertainty quantification of linear and nonlinear dynamical systems. Comparisons have also been drawn with Recurrent Neural Network results and with results obtained from Proper Orthogonal Decomposition based Gaussian process. The results obtained indicate that the DeepONet architecture is accurate as well as efficient. Moreover, DeepONet posses zero shot learning capabilities and hence, a trained model easily generalizes to unseen and new environment with no further training. • We investigate DeepONet for time-dependent reliability analysis. • DeepOnet learns operator and allows zero shot learning. • DeepONet accurately captures probability of failure and PDF of FPFT. • DeepONet is highly efficient and yields accurate results.
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