Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
Scientific machine learning
2 Pith papers cite this work, alongside 4 external citations. Polarity classification is still indexing.
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PyCC.id packages a hypothesis-driven method using identifiable ODE skeletons for equation discovery from data, supporting multiple paradigms like neural networks and sparse regression.
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Continuous ageing trajectory representations for knee-aware lifetime prediction of lithium-ion batteries across heterogeneous dataset
Continuous trajectory representations of lithium-ion battery aging enable consistent knee-point detection and early remaining useful life predictions that remain robust across heterogeneous datasets.
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PyCC.id: A package for hypothesis-driven equation discovery with structural identifiability
PyCC.id packages a hypothesis-driven method using identifiable ODE skeletons for equation discovery from data, supporting multiple paradigms like neural networks and sparse regression.