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• W4376273071 abstract "Estimating the average health degradation of a new battery design is a crucial objective for manufacturers to estimate its value. Furthermore, to quantify financial risks, associated uncertainties should be modeled precisely. From a data-driven perspective, Gaussian process regression (GPR) is often a method of choice since it simultaneously learns complex models and naturally includes uncertainties. However, GPR methods generally rely on a stationarity assumption which imposes severe constraints on uncertainties. In this paper, we illustrate the limits of standard GPR and show that the Chained Gaussian processes, a more general framework introduced in the Machine learning community, is a useful alternative, allowing more accurate quantification of uncertainties." @default.
• W4376273071 created "2023-05-13" @default.
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• W4376273071 date "2023-09-01" @default.
• W4376273071 modified "2023-10-09" @default.
• W4376273071 title "Chained Gaussian processes to estimate battery health degradation with uncertainties" @default.
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• W4376273071 doi "https://doi.org/10.1016/j.est.2023.107443" @default.
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