This work presents a novel physics-informed deep learning based super-resolution framework to reconstruct high-resolution deformation fields from low-resolution counterparts, obtained from coarse mesh simulations or experiments. We leverage the governing equations and boundary conditions of the physical system to train the model without using any high-resolution labeled data. The proposed approach is applied to obtain the super-resolved deformation fields from the low-resolution stress and displacement fields obtained by running simulations on a coarse mesh for a body undergoing linear elastic deformation. We demonstrate that the super-resolved fields match the accuracy of an advanced numerical solver running at 400 times the coarse mesh resolution, while simultaneously satisfying the governing laws. A brief evaluation study comparing the performance of two deep learning based super-resolution architectures is also presented.
翻译:这项工作提出了一个新的基于物理知识的基于深深学习的超分辨率框架,用以从低分辨率的模拟或实验中从低分辨率的对口单位重建高分辨率变形场。我们利用物理系统的治理方程式和边界条件来培训模型,而不使用任何高分辨率标签数据。拟议方法用于从低分辨率压力和变形场获取超分辨率变形场,通过对正经历线性变形的机体的粗微网块进行模拟获得。我们证明,超溶化场与以粗度网格分辨率400倍运行的高级数字解析器的准确性相匹配,同时满足了管理法。还介绍了对两个基于深层学习的超分辨率结构进行业绩比较的简要评价研究。