Multiresolution clustering analysis for efficient modeling of hierarchical material systems

Direct representation of material microstructure in a macroscale simulation is prohibitively expensive, if even possible, with current methods. However, the information contained in such a representation is highly desirable for tasks such as material/alloy design and manufacturing process control. In this paper, a mechanistic machine learning framework is developed for fast multiscale analysis of material response and structure performance. The new capabilities stem from three major factors: (1) the use of an unsupervised learning (clustering)-based discretization to achieve significant order reduction at both macroscale and microscale; (2) the generation of a database of interaction tensors among discretized material regions; (3) concurrent multiscale response prediction to solve the mechanistic equations. These factors allow for an orders-of-magnitude decrease in the computational expense compared to FEn, n $$\ge $$ 2. This method provides sufficiently high fidelity and speed to reasonably conduct inverse modeling for the challenging tasks mentioned above.