论文标题
评估deponet的随机非线性动力学系统的可靠性分析
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
论文作者
论文摘要
随机强迫功能的结构系统的时间依赖性可靠性分析和不确定性量化是一项艰巨的努力，因为它需要相当大的计算时间。我们研究了最近提出的deponet在解决时间依赖性的可靠性分析和受到随机负载的系统的不确定性量化方面的疗效。与传统的机器学习和深度学习算法不同，DeepOnet学习是一个操作员网络，并且学习了功能映射的功能，因此非常适合传播从随机强迫函数到输出响应的不确定性。我们使用deeponet为正在考虑的动力系统构建替代模型。已经进行了多个案例研究，涉及玩具和基准问题，以检查deponet在时间依赖性可靠性分析中的疗效以及线性和非线性动力学系统的不确定性量化。获得的结果表明，Deponet架构既准确又有效。此外，DeWonet具有零射击学习能力，因此，受过训练的模型很容易概括为看不见和新的环境，而无需进一步的培训。
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.
