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Non-judgemental Dynamic Fuel Cycle Benchmarking
[article]
2015
arXiv
pre-print
This paper presents a new fuel cycle benchmarking analysis methodology by coupling Gaussian process regression, a popular technique in Machine Learning, to dynamic time warping, a mechanism widely used in speech recognition. Together they generate figures-of-merit that are applicable to any time series metric that a benchmark may study. The figures-of-merit account for uncertainty in the metric itself, utilize information across the whole time domain, and do not require that the simulators use
arXiv:1511.09095v1
fatcat:bmwvyaojufdorkftwl6q6fqt3m