论文标题
以生成模型为数字双胞胎的基础
On generative models as the basis for digital twins
论文作者
论文摘要
为生成模型提出了一个框架，作为数字双胞胎或结构镜子的基础。该提案基于这样的前提：确定性模型无法说明大多数结构建模应用中存在的不确定性。这里考虑了两种不同类型的生成模型。第一个是基于物理学的模型，基于随机有限元（SFE）方法，当建模具有物质和加载不确定性的结构时，该模型被广泛使用。可以根据结构的数据校准此类模型，如果建模准确地捕获结构的真实基础物理，则预计将胜过任何其他模型。通过应用于具有随机材料特性的线性结构来说明SFE模型作为数字镜的潜在用途。对于此类模型的物理表述不足的情况，使用机器学习和有条件的生成对抗网络（CGAN）提出了数据驱动的框架。后一种算法用于学习具有物质非线性和不确定性结构中兴趣量的分布。对于这项工作中考虑的示例，数据驱动的CGAN模型模型优于基于物理的方法。最后，显示了一个示例，其中两种方法是耦合的，以便证明混合模型方法。
A framework is proposed for generative models as a basis for digital twins or mirrors of structures. The proposal is based on the premise that deterministic models cannot account for the uncertainty present in most structural modelling applications. Two different types of generative models are considered here. The first is a physics-based model based on the stochastic finite element (SFE) method, which is widely used when modelling structures that have material and loading uncertainties imposed. Such models can be calibrated according to data from the structure and would be expected to outperform any other model if the modelling accurately captures the true underlying physics of the structure. The potential use of SFE models as digital mirrors is illustrated via application to a linear structure with stochastic material properties. For situations where the physical formulation of such models does not suffice, a data-driven framework is proposed, using machine learning and conditional generative adversarial networks (cGANs). The latter algorithm is used to learn the distribution of the quantity of interest in a structure with material nonlinearities and uncertainties. For the examples considered in this work, the data-driven cGANs model outperform the physics-based approach. Finally, an example is shown where the two methods are coupled such that a hybrid model approach is demonstrated.
