{"id":30278281,"url":"https://github.com/bitlap/paper-notes","last_synced_at":"2026-02-10T05:32:32.108Z","repository":{"id":307996741,"uuid":"1031285318","full_name":"bitlap/paper-notes","owner":"bitlap","description":"论文阅读计划及笔记","archived":false,"fork":false,"pushed_at":"2025-08-03T13:57:56.000Z","size":4,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-08-03T15:11:24.581Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bitlap.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-08-03T12:22:52.000Z","updated_at":"2025-08-03T13:57:59.000Z","dependencies_parsed_at":"2025-08-03T15:12:07.658Z","dependency_job_id":"a525796a-0bc4-4e70-9a3b-ff8252357ac1","html_url":"https://github.com/bitlap/paper-notes","commit_stats":null,"previous_names":["bitlap/paper-notes"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/bitlap/paper-notes","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bitlap%2Fpaper-notes","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bitlap%2Fpaper-notes/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bitlap%2Fpaper-notes/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bitlap%2Fpaper-notes/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bitlap","download_url":"https://codeload.github.com/bitlap/paper-notes/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bitlap%2Fpaper-notes/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270709088,"owners_count":24631992,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-16T02:00:11.002Z","response_time":91,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-08-16T12:35:44.112Z","updated_at":"2026-02-10T05:32:27.875Z","avatar_url":"https://github.com/bitlap.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"论文阅读计划及笔记\n---\n\n# 电池论文\n\n**失效和热失控相关**\n\n**寿命和性能相关**\n- [Calendar aging model for lithium-ion batteries considering the influence of cell characterization](https://doi.org/10.1016/j.est.2021.103506)\n- [Degradation diagnostics for lithium ion cells](https://doi.org/10.1016/j.jpowsour.2016.12.011)\n- [Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries](https://doi.org/10.1149/2.0281914jes)\n- [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)\n- [Lithium-ion battery degradation: how to model it](https://doi.org/10.1039/D2CP00417H)\n- [Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis](https://doi.org/10.1038/s41467-024-48779-z)\n- [Physics-Informed Neural Networks for State of Health Estimation in Lithium-Ion Batteries](https://doi.org/10.1149/1945-7111/acf0ef)\n- [Learning the P2D Model for Lithium-Ion Batteries with SOH Detection](https://doi.org/10.48550/arXiv.2502.14147)\n- [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)\n- [A modified Doyle-Fuller-Newman model enables the macroscale physical simulation of dual-ion batteries](https://doi.org/10.1016/j.jpowsour.2023.233429)\n- [Lithium ion battery degradation: what you need to know](https://doi.org/10.1039/D1CP00359C)\n- [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)\n- [Review—“Knees” in Lithium-Ion Battery Aging Trajectories](https://doi.org/10.1149/1945-7111/ac6d13)\n- [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)\n- [Algorithm to Determine the Knee Point on Capacity Fade Curves of Lithium-Ion Cells](https://doi.org/10.3390/en12152910)\n- [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)\n- [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)\n\n# 其他论文\n - [Transformer Explainer: Interactive Learning of Text-Generative Models](https://arxiv.org/abs/2408.04619)\n - [A physics-informed neural network enhanced importance sampling (PINN-IS) for data-free reliability analysis](https://doi.org/10.1016/j.probengmech.2024.103701)\n - [Respecting causality is all you need for training physics-informed neural networks](https://arxiv.org/abs/2203.07404)\n - [From local explanations to global understanding with explainable AI for trees](https://doi.org/10.1038/s42256-019-0138-9) \n - [Accurate predictions on small data with a tabular foundation model](https://doi.org/10.1038/s41586-024-08328-6) \n - [A Unified Approach to Interpreting Model Predictions](https://arxiv.org/abs/1705.07874) \n - [Attention Is All You Need](https://arxiv.org/abs/1706.03762) \n - [Deeppipe: A two-stage physics-informed neural network for predicting mixed oil concentration distribution](https://doi.org/10.1016/j.energy.2023.127452)\n - [Vision Transformers: State of the Art and Research Challenges](https://arxiv.org/abs/2207.03041)\n - [Discovery of partial differential equations from highly noisy and sparse data with physics-informed information criterion](https://doi.org/10.48550/arXiv.2208.03322)\n - [A Kernel Approach for PDE Discovery and Operator Learning](https://doi.org/10.48550/arXiv.2210.08140)\n - [Noise-aware physics-informed machine learning for robust PDE discovery](https://doi.org/10.1088/2632-2153/acb1f0)\n - [Data-driven discovery of partial differential equations](https://doi.org/10.1126/sciadv.1602614)\n - [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)\n - [Physics-informed learning of governing equations from scarce data](https://doi.org/10.1038/s41467-021-26434-1)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbitlap%2Fpaper-notes","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbitlap%2Fpaper-notes","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbitlap%2Fpaper-notes/lists"}