论文标题
非线性振动多物理学微结构的虚拟双胞胎：基于物理学和基于深度学习的方法
Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches
论文作者
论文摘要
微电动机械系统是复杂的结构，通常涉及几何和多物理性质的非线性，它们被用作无数应用中的传感器和执行器。从全阶表示开始，我们采用深度学习技术来生成准确，高效和实时降低的订单模型，以用作虚拟双胞胎的模拟和优化高级复杂系统。我们广泛测试了在微龙，拱形和陀螺仪上提出的程序的可靠性，还显示了复杂的动力学演变，例如内部共振。特别是，我们讨论了深度学习技术的准确性及其复制和收敛到使用最近开发的直接参数化方法预测的不变流形的能力，该方法允许提取大量有限元模型的非线性正常模式。最后，通过解决机电陀螺仪，我们表明，非侵入性深度学习方法很容易概括为复杂的多物理问题
Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep learning techniques to generate accurate, efficient and real-time reduced order models to be used as virtual twin for the simulation and optimization of higher-level complex systems. We extensively test the reliability of the proposed procedures on micromirrors, arches and gyroscopes, also displaying intricate dynamical evolutions like internal resonances. In particular, we discuss the accuracy of the deep learning technique and its ability to replicate and converge to the invariant manifolds predicted using the recently developed direct parametrization approach that allows extracting the nonlinear normal modes of large finite element models. Finally, by addressing an electromechanical gyroscope, we show that the non-intrusive deep learning approach generalizes easily to complex multiphysics problems
