The field of computational modeling of materials and structures is rapidly advancing, with a focus on developing innovative methods and techniques to simulate complex phenomena. Recent developments have centered around the creation of novel finite element methods, such as the Crack Element Method, and the application of peridynamic theory to model deformation and damage in microchannels. Additionally, researchers are exploring the use of machine learning and data-driven approaches to analyze and understand the behavior of materials and structures. Noteworthy papers in this area include: A Practical Finite Element Approach for Simulating Dynamic Crack Growth in Cu/Ultra Low-k Interconnect Structures, which presents a novel finite element method for simulating dynamic crack growth. A fluid--peridynamic structure model of deformation and damage of microchannels, which develops a one-dimensional model for simulating fluid-structure interaction in microchannels. Homogenization rates of beam lattices to micropolar continua, which rigorously analyzes the homogenization process of a beam lattice to a continuum. A GPU-Accelerated Three-Dimensional Crack Element Method for Transient Dynamic Fracture Simulation, which presents a novel three-dimensional Crack Element Method for simulating transient dynamic crack propagation. Multi-Marginal Stochastic Flow Matching for High-Dimensional Snapshot Data at Irregular Time Points, which presents a novel extension of simulation-free score and flow matching methods to the multi-marginal setting. Fuzzy Decisions on Fluid Instabilities: Autoencoder-Based Reconstruction meets Rule-Based Anomaly Classification, which presents a hybrid framework that combines unsupervised autoencoder models with a fuzzy inference system to generate and interpret anomaly maps. How and Why: Taming Flow Matching for Unsupervised Anomaly Detection and Localization, which proposes a new paradigm for unsupervised anomaly detection and localization using Flow Matching.