e-journal
Cross-Domain Model Building and Validation (CDMV): A New Modeling Strategy to Reinforce Understanding of Nanomanufacturing Processes
Understanding nanostructure growth faces issues of limited data, lack of physical knowledge, and large process uncertainties. These issues result in modeling difficulty because a large pool of candidate models almost fit the data equally well. Through the Integrated Nanomanufacturing and Nanoinformatics
(INN) strategy, we derive the process models from physical and statistical domains, respectively, and reinforce the understanding of growth processes by identifying the common model structure
across two domains. This cross-domain model building strategy essentially validates models by domain knowledge rather than by (unavailable) data. It not only increases modeling confidence under large uncertainties, but also enables insightful physical understanding of the growth kinetics. We present this method by studying the weight growth kinetics of silica nanowire under two temperature conditions. The derived nanowire growth model is able to provide physical insights for prediction and control under
uncertainties. Note to Practitioners—Nanostructures have great electrical, mechanical, optical, and biological applications due to their unique properties. To improve the yield of their mass production, predictive modeling, and control method is essential. The major difficulty of establishing such models is relatively little physical understanding of growth process as well as limited data with large variability.
This paper provides a cross-domain modeling approach which is tailored for this application and validates its applicability by using a silica nanowire weight growth example. Index Terms—Cross-domain modeling and validation, nanomanufacturing, nanostructure growth, physical mechanism selection.
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