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) Artificial Intelligence (AI)-based predictions. These instruments raise concerns about safety because they rely on black-box AI models and do not contain any guardrails if physical and/or digital parts
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301.975.6050 Jan Obrzut jan.obrzut@nist.gov 301.975.6845 Description As part of a collaborative NIST-wide program involving structural characterization, modeling, and high-throughput microwave measurement, we
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strain, or outright violate, the assumptions underlying the statistical models currently used. This project centers around investigating and advancing our new and improved method for the analysis of clock
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even several million atoms on an ordinary computer. It links the different length scales smoothly and seamlessly.Such a model should be useful for many industrial applications of nanodiamnds
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absorption fine structure), development of data-analysis approaches and computer software for simultaneous structural refinements using multiple types of data combined with ab initio theoretical modeling
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RAP opportunity at National Institute of Standards and Technology NIST Mathematical Modeling, Analysis, and Uncertainty Quantification Location Information Technology Laboratory, Applied and
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://jarvis.nist.gov/) infrastructure uses a variety of methods such as density functional theory, graph neural networks, computer vision, classical force field, and natural language processing. We are currently
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strain rates and temperatures. Identifying material properties and constitutive model parameters from the integrated data sets currently focuses on the finite element model updating (FEMU) method, but
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; Electron microscopy; X-ray diffraction; X-ray computed tomography; Mechanical properties; Fatigue; Fracture; Modeling; Atom probe; Microstructure; Processing; Eligibility Citizenship: Open to U.S. citizens
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academic communities. However, there are many practical impediments which need to be addressed to realize the full potential of SDD/EDS. We work on many different aspects of the problem: Modeling X-ray