https://github.com/bitlap/paper-notes
论文阅读计划及笔记
https://github.com/bitlap/paper-notes
Last synced: 4 months ago
JSON representation
论文阅读计划及笔记
- Host: GitHub
- URL: https://github.com/bitlap/paper-notes
- Owner: bitlap
- Created: 2025-08-03T12:22:52.000Z (11 months ago)
- Default Branch: master
- Last Pushed: 2025-08-03T13:57:56.000Z (11 months ago)
- Last Synced: 2025-08-03T15:11:24.581Z (11 months ago)
- Homepage:
- Size: 3.91 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
论文阅读计划及笔记
---
# 电池论文
**失效和热失控相关**
**寿命和性能相关**
- [Calendar aging model for lithium-ion batteries considering the influence of cell characterization](https://doi.org/10.1016/j.est.2021.103506)
- [Degradation diagnostics for lithium ion cells](https://doi.org/10.1016/j.jpowsour.2016.12.011)
- [Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries](https://doi.org/10.1149/2.0281914jes)
- [Lithium-ion battery degradation modelling using universal differential equations: Development of a cost-effective parameterisation methodology](https://doi.org/10.1016/j.apenergy.2024.125221)
- [Lithium-ion battery degradation: how to model it](https://doi.org/10.1039/D2CP00417H)
- [Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis](https://doi.org/10.1038/s41467-024-48779-z)
- [Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries](https://doi.org/10.1149/1945-7111/acf0ef)
- [Learning the P2D Model for Lithium-Ion Batteries with SOH Detection](https://doi.org/10.48550/arXiv.2502.14147)
- [A Multilayer Doyle-Fuller-Newman Model to Optimise the Rate Performance of Bilayer Cathodes in Li Ion Batteries](https://doi.org/10.1149/1945-7111/ad5767)
- [A modified Doyle-Fuller-Newman model enables the macroscale physical simulation of dual-ion batteries](https://doi.org/10.1016/j.jpowsour.2023.233429)
- [Lithium ion battery degradation: what you need to know](https://doi.org/10.1039/D1CP00359C)
- [A Single Particle model with electrolyte and side reactions for degradation of lithium-ion batteries](https://doi.org/10.1016/j.apm.2022.12.009)
- [Review—“Knees” in Lithium-Ion Battery Aging Trajectories](https://doi.org/10.1149/1945-7111/ac6d13)
- [Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells](https://doi.org/10.1016/j.egyai.2020.100006)
- [Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells](https://doi.org/10.3390/en12152910)
- [Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells](https://doi.org/10.1016/j.egyai.2020.100006)
- [Dynamic double similarity fusion based on ΔQ power law for early-cycle RUL prediction of lithium-ion batteries](https://doi.org/10.1016/j.ijoes.2025.101102)
# 其他论文
- [Transformer Explainer: Interactive Learning of Text-Generative Models](https://arxiv.org/abs/2408.04619)
- [A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis](https://doi.org/10.1016/j.probengmech.2024.103701)
- [Respecting causality is all you need for training physics-informed neural networks](https://arxiv.org/abs/2203.07404)
- [From local explanations to global understanding with explainable AI for trees](https://doi.org/10.1038/s42256-019-0138-9)
- [Accurate predictions on small data with a tabular foundation model](https://doi.org/10.1038/s41586-024-08328-6)
- [A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874)
- [Attention Is All You Need](https://arxiv.org/abs/1706.03762)
- [Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution](https://doi.org/10.1016/j.energy.2023.127452)
- [Vision Transformers: State of the Art and Research Challenges](https://arxiv.org/abs/2207.03041)
- [Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion](https://doi.org/10.48550/arXiv.2208.03322)
- [A Kernel Approach for PDE Discovery and Operator Learning](https://doi.org/10.48550/arXiv.2210.08140)
- [Noise-aware physics-informed machine learning for robust PDE discovery](https://doi.org/10.1088/2632-2153/acb1f0)
- [Data-driven discovery of partial differential equations](https://doi.org/10.1126/sciadv.1602614)
- [DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data](https://doi.org/10.48550/arXiv.1908.04463)
- [Physics-informed learning of governing equations from scarce data](https://doi.org/10.1038/s41467-021-26434-1)