E-Mail IMAGE: In an example shown with Old Main -- Penn State's main administration building on the University Park campus -- the researchers' algorithm takes a simple image of the material microstructure... view more Credit: Pranav Milind Khanolkar, Penn State Various software packages can be used to evaluate products and predict failure; however, these packages are extremely computationally intensive and take a significant amount of time to produce a solution. Quicker solutions mean less accurate results. To combat this issue, a team of Penn State researchers studied the use of machine learning and image colorization algorithms to ease computational load, maintain accuracy, reduce time and predict strain fields for porous materials. They published their work in the