• 1. Reliability and Resilience Engineering for Smart City Critical Infrastructures Under Climate Change
  气候变化下智慧城市关键基础设施的可靠性与韧性工程
  3. Cybersecurity and Functional Safety Co-Design for Autonomous and Intelligent Transportation Systems
  自主智能交通系统的网络安全与功能安全协同设计
  4. Reliability Modeling and Life-Cycle Optimization for New Energy Vehicle Charging Infrastructure
  新能源汽车充电基础设施的可靠性建模与全生命周期优化
  5. Data-Driven Reliability Engineering for Low-Altitude Economy: Drones and eVTOL Aircraft
  面向低空经济的数据驱动可靠性工程：无人机与电动垂直起降飞行器
  6. Physics-Informed Digital Twin for Reliability Assessment of Nuclear and Renewable Energy Systems
  基于物理信息的数字孪生在核电与可再生能源系统可靠性评估中的应用
  7. Human-AI Collaboration in Reliability Risk Analysis and Maintenance Decision-Making
  人机协同在可靠性风险分析与维修决策中的应用
  8. Uncertainty Quantification for Multiphysics Reliability of Semiconductor and Electronic Manufacturing Systems
  半导体与电子制造系统多物理场可靠性的不确定性量化
  9. Resilience and Risk Mitigation of Industrial Cyber-Physical Systems Against Cascading Failures
  工业信息物理系统抵御级联失效的韧性与风险防控
  10. Generative AI and Large Language Models for Automated FMEA and Reliability Data Analytics
  生成式 AI 与大语言模型在自动化 FMEA 及可靠性数据分析中的应用

•  Proposal Form Download Here
  
• - Invited sessions consist of 5-8 thematically related invited papers. Invited papers are submitted and reviewed following the same process as contributed papers, and are included in the conference proceeding.
• Special-session proposals should be submitted by the prospective organizer(s) who will commit to promoting and handling the review process of their special session as Chairs or Co-Chairs of the event.
  
  Please provide the following information:
  ● Title;
  ● Name(s) of organizer(s);
  ● Email of main contact person;
  ● Brief bio(s) of organizer(s);
  ● Brief description;
  ● Related topics;
  ● Potential participants;

•  Confirmed Special Sessions
  
• - SS1: Industrial AI-Driven Operation and Maintenance
  - SS2: Multiphysics Modeling, Uncertainty Quantification, and Intelligent Health Management for Highly Reliable Components and Systems
  - SS3: Physics-Informed Machine Learning for Prognostics and Health Management of Industrial Equipment
  - SS4: Innovation and Practice of Data-Driven Soft Computing Methods in Renewable Energy Forecasting
  - SS5: Multimodal Foundation Models for Digital Twin–Integrated Prognostics and Health Management of Complex Engineering Systems
  - SS6: Explainable AI for Prognostics and Health Management of Aerospace and Avionic Systems
  - SS7: Artificial Intelligence for Fault Diagnosis and Prognostics of Complex Engineering Systems
  - SS8: Belief Reliability Theory and Applications: Recent Advances, Methods, and Practices
  - SS9: Physics-Informed Digital Twin for Reliability Assessment of Nuclear and Renewable Energy Systems