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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.