2025-04-09 09:00:00
We investigate the fragmentation response of a thin ring subjected to radially expanding loads. Material strength is represented as a random field. By adjusting the covariance function, we can systematically incorporate various forms of material heterogeneity, such as the length scale of variations, roughness,…
The advent of big data is revolutionizing scientific discovery, enabling the development of novel models, refinement of existing frameworks, and precise uncertainty quantification. Simultaneously, advancements in scientific machine learning have unlocked powerful tools for solving inverse problems, especially in scenarios involving complex systems with…
We developed an image-based convolutional neural network (CNN) designed for quantitative, time-resolved measurement of fragmentation behavior in opaque brittle materials using ultra-high-speed optical imaging. Building on prior work with the U-net model, we trained binary, 3-class, and 5-class models via supervised learning, using data…
