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ROBIN : A Benchmark for Robustness to Individual Nuisances in Real-World Out-of-Distribution Shifts
Current approaches to enhance OOD robustness using strong data augmentation have only marginal effects in real-world OOD scenarios, and sometimes even reduce the OOD performance.
Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships
A binary alloy (uranium-molybdenum) that is currently under development as a nuclear fuel was studied for the purpose of developing an improved machine learning approach to image recognition, characterization, and building predictive capabilities linking microstructure to processing conditions.
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Here, we apply these well-established methods to develop an approach to microstructure quantification for kinetic modeling of a discontinuous precipitation reaction in a case study on the uranium-molybdenum system.
