The Center for Materials Data Science for Reliability and Degradation (MDS-Rely) seeks to better understand and improve the reliability and lifetime of essential materials using rigorous data science methods and analytics. Data science-informed approaches can revolutionize the development, manufacturing, and lifecycle applications of materials, parts, and products for U.S. infrastructures in energy, defense, and transportation. MDS-Rely has the potential to transform product reliability by creating technologies that enable unprecedented lifetimes and durability in extreme environments. MDS-Rely research will also deepen the understanding of active degradation and failure mechanisms and improve product design and manufacturing for long service lifetimes.
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MDS-Rely’s research is grounded in three core competencies:
These competencies support the research areas:
These research areas allow MDS-Rely to focus on these three current value chains:
MDS-Rely research involves statistical and machine learning, time-series modeling, spectral modeling, supervised and unsupervised machine learning of images, network graph modeling, and process modeling — and ultimately allows for the creation of transferrable codes and research pipelines.
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