The Materials Research Institute and the College of Engineering have announced the recipients of the Materials Matter at the Human Level seed grants. The grants were developed to continue the history of MRI and the College of Engineering partnering to fund materials projects that benefit humankind, including those aimed at improving the health and economic development of under-resourced populations.
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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