论文标题
多余性多物理数据的高弹性问题的加速混合数据驱动/基于模型的方法
An accelerated hybrid data-driven/model-based approach for poroelasticity problems with multi-fidelity multi-physics data
论文作者
论文摘要
我们提出了一种混合模型/无模型数据驱动的方法来解决毛弹性问题。扩展数据驱动的建模框架起源于Kirchdoerfer和Ortiz（2016），我们介绍了一种无模型和两个基于混合模型的/数据驱动的公式，能够模拟流体浸入流体浸润的多孔媒体的耦合扩散型具有不同量的可用数据。为了提高无模型数据搜索的效率，我们引入了通过k维树搜索加速的距离最小化算法。为了处理固体弹性和流体液压本质响应的不同保真度，我们引入了杂交模型，其中固体和流体求解器可以根据数据的可用性和性能从基于模型的方法切换到无模型方法。数值实验旨在验证实现并将提出模型的性能与其他替代方案进行比较。
We present a hybrid model/model-free data-driven approach to solve poroelasticity problems. Extending the data-driven modeling framework originated from Kirchdoerfer and Ortiz (2016), we introduce one model-free and two hybrid model-based/data-driven formulations capable of simulating the coupled diffusion-deformation of fluid-infiltrating porous media with different amounts of available data. To improve the efficiency of the model-free data search, we introduce a distance-minimized algorithm accelerated by a k-dimensional tree search. To handle the different fidelities of the solid elasticity and fluid hydraulic constitutive responses, we introduce a hybridized model in which either the solid and the fluid solver can switch from a model-based to a model-free approach depending on the availability and the properties of the data. Numerical experiments are designed to verify the implementation and compare the performance of the proposed model to other alternatives.
