随机非线性动力系统可靠性分析的DeepONet评估
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
摘要：
随机荷载作用下结构系统的时变可靠性分析和不确定性量化是一项具有挑战性的工作，因为它需要大量的计算时间。我们研究了最近提出的DeepONet在解决随机加载系统的时间相关可靠性分析和不确定性量化方面的有效性。与传统的机器学习和深度学习算法不同，DeepONet学习是一个算子网络，并学习函数到函数的映射，因此非常适合将不确定性从随机强制函数传播到输出响应。我们使用DeepONet为所考虑的动态系统构建代理模型。多个案例研究，涉及玩具和基准问题，已经进行了检查DeepONet在线性和非线性动力系统的时间相关可靠性分析和不确定性量化的功效。结果表明，DeepONet架构是准确和高效的。此外，DeepONet具有zero shot学习能力，因此，经过训练的模型很容易推广到看不见的新环境，而无需进一步训练。
英文摘要：
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 learns is a 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. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
