The field of material science and elasticity is experiencing significant developments in computational methods, driven by the need for efficient and accurate simulations of complex materials and structures. Recent research has focused on improving numerical discretization methods, developing new algorithms for constitutive model discovery, and creating innovative solutions for tensor completion and low-rank approximation. These advances have the potential to revolutionize the field, enabling faster and more accurate predictions of material behavior and properties. Noteworthy papers in this area include: Automated Constitutive Model Discovery by Pairing Sparse Regression Algorithms with Model Selection Criteria, which presents a fully automated framework for constitutive model discovery, and Extended Low-Rank Approximation Accelerates Learning of Elastic Response in Heterogeneous Materials, which introduces a framework for efficiently learning structure-property linkages governing mechanical behavior.