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Xinfan Lin

As seen in: Nature, arXiv
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Articles

Research on creep life prediction of entangled metallic wire material based on performance degradation parameters and time–temperature superposition principle

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By Fuqiang Lai, Xinfan Lin, Linfeng Xie, Zhihuang Qiu, Liangwan Chen, Guilin Gao
| Nature Verified
Abstract The entangled metallic wire material (EMWM) exhibits the significant advantages, including high damping performance, resistance to aging, and strong environmental adaptability, making it widely applicable across various fields. As application fields of EMWM have been expanded, examining the service life of EMWM in various working conditions has become increasingly critical.
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[2511.09016] Assumed Density Filtering and Smoothing with Neural Network Surrogate Models

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By Xinfan Lin
| arXiv
arXiv:2511.09016 (eess) View PDF HTML (experimental) Subjects: Systems and Control (eess.SY); Machine Learning (cs.LG) Cite as: arXiv:2511.09016 [eess.SY] (or arXiv:2511.09016v1 [eess.SY] for this version) https://doi.org/10.48550/arXiv.2511.09016 Submission history From: Simon Kuang [ view email] [v1] Wed, 12 Nov 2025 06:08:53 UTC (540 KB) Bibliographic Tools Bibliographic Explorer Toggle Bibliographic Explorer () Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps...
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[2505.06473] Joint Parameterization of Hybrid Physics-Based and Machine Learning Li-Ion Battery Model

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By Xinfan Lin
| arXiv
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