Sort by
Refine Your Search
-
techniques. Developed models will be used to optimize processing and conventional alloy compositions for additive manufacturing. T. Keller, G. Lindwall, S. Ghosh, L. Ma, B.M. Lane, F. Zhang, U.R. Kattner
-
RAP opportunity at National Institute of Standards and Technology NIST Applied Optimization and Simulation Location Information Technology Laboratory, Applied and Computational Mathematics
-
Machine Learning-driven Autonomous Systems for Materials Discovery and Optimization NIST only participates in the February and August reviews. We are developing machine learning-driven autonomous
-
Machine Learning for High Throughput Materials Discovery and Optimization Applications NIST only participates in the February and August reviews. We are developing machine learning algorithms
-
RAP opportunity at National Institute of Standards and Technology NIST Designing Liquid Scintillators for Optimal Light Yield, Pulse Shape Discrimination, and Neutron Sensitivity
-
. The NIST channel sounding measurement team specializes in the development and use of instrumentation in the 10s of GHz based on phased array antennas that is optimized to capture dynamically evolving
-
aptamer conformation changes. Method development efforts should focus on the incorporation of a robust and optimized experimental design aimed at assessing the sources of variability, repeatability, and
-
. Developing advanced machine learning models and physics-informed algorithms for analyzing high-speed XRD data and phase transformation kinetics assessment. Applying the insights gained to optimize AM
-
impediments to meeting the desired manufacturing and performance standards. Digital twins (DT) are being adopted in the AM industry to optimize the entire manufacturing process and enable products with high
-
Description Mathematical modeling forms the basis for understanding, simulating, optimizing and controlling numerous scientific phenomenon and associated measurements. Mathematical models frequently take the