Extended Low-Rank Approximation Accelerates Learning of Elastic Response in Heterogeneous Materials

ArXi:2509.20276v2 Announce Type: replace Predicting how the microstructure governs the mechanical response of heterogeneous materials is essential for optimizing design and performance. Yet this task remains difficult due to the complex, high dimensional nature of microstructural features. Relying on physics based simulations to probe the microstructural space is computationally prohibitive. This motivates the development of computational tools to efficiently learn structure property linkages governing mechanical behavior.