18th PARIS International Conference on “Artificial Intelligence, Energy & Manufacturing Engineering” (AIEME-25) scheduled on Dec. 3-5, 2025 Paris (France)

Topics of Interest for Submission include, but are Not Limited to: I. Artificial Intelligence (AI) - Foundational and Applied: - Machine Learning (ML) & Deep Learning (DL): - Supervised, unsupervised, reinforcement learning algorithms. - Neural networks, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformers. - Generative AI (GenAI) for design, optimization, and content creation in engineering. - Physics-informed AI/Machine Learning (PIML) for integrating physical laws into AI models. - Explainable AI (XAI) and AI ethics in critical applications. - Data Science & Analytics: - Big data management, processing, and analysis for large-scale engineering data. - Predictive analytics, forecasting, anomaly detection. - Data visualization and interpretation for engineers. - Data fusion from diverse sensors and sources. - AI for Control & Automation: - Intelligent control systems, adaptive control, optimal control. - Robotics and autonomous systems (industrial robots, drones, autonomous vehicles in industrial settings). - Human-robot interaction and collaboration (cobots). - AI Architectures & Platforms: - Edge AI, fog computing, and cloud computing for industrial applications. - AI hardware accelerators (GPUs, TPUs, neuromorphic chips). - AI software frameworks and tools. II. AI in Energy Systems: - Smart Grids & Energy Management: - AI for optimizing energy generation, transmission, and distribution. - Predictive maintenance of grid infrastructure (transformers, power lines). - Demand forecasting and response management. - Load balancing and peak shaving using AI. - Microgrid optimization and control. - Renewable Energy Integration & Optimization: - Forecasting wind power, solar irradiance, and hydropower generation. - AI for optimal siting of renewable energy facilities. - Hybrid renewable energy systems management. - Energy storage optimization (batteries, hydrogen, pumped hydro). - Energy Efficiency & Sustainability: - AI for optimizing energy consumption in industrial facilities, buildings, and transportation. - Carbon emissions monitoring, prediction, and reduction strategies using AI. - AI for resource management and waste reduction in energy production. - Energy Infrastructure & Security: - Predictive maintenance for power plants, turbines, and other energy assets. - Cybersecurity for critical energy infrastructure (SCADA systems, smart meters). - AI for fault detection and diagnosis in complex energy systems. - New Energy Technologies: - AI in nuclear fusion research. - AI for advanced materials in energy (e.g., better batteries, solar cells). - Optimization of hydrogen production, storage, and distribution. III. AI in Manufacturing Engineering: - Smart Manufacturing & Industry 4.0/5.0: - Digital twins and cyber-physical systems for real-time monitoring and control. - AI for predictive maintenance of manufacturing machinery (e.g., CNC machines, assembly lines). - Autonomous manufacturing systems and intelligent automation. - Human-AI collaboration in production environments (Operator 5.0). - Production Optimization & Control: - AI-driven production scheduling and resource allocation. - Real-time process optimization and adaptive control. - Quality control and defect detection using computer vision and sensor data. - Root cause analysis of production issues with AI. - Advanced Manufacturing Processes: - AI for additive manufacturing (3D printing) – design, process control, quality assurance. - Robotics and automation in assembly, welding, material handling, and finishing. - AI for generative design of components and products. - Simulation and modeling of manufacturing processes using AI. - Supply Chain & Logistics: - AI for optimizing supply chain planning, logistics, and inventory management. - Demand forecasting and risk management in supply chains. - Blockchain for secure and transparent supply chain operations. - Workforce & Human Factors: - AI for skill development and training in manufacturing. - Ergonomics and safety in AI-driven manufacturing environments. - AI-assisted decision-making for human operators. - Sustainable Manufacturing: - AI for reducing waste and optimizing material usage. - Circular economy principles in manufacturing driven by AI. - Energy efficiency in factories and production lines. IV. Cross-Cutting & Interdisciplinary Themes: - Integration of AI, Energy, and Manufacturing: Case studies and real-world applications demonstrating synergistic benefits. - Cybersecurity for AI-enabled Systems: Protecting industrial and energy infrastructure from AI-driven threats. - Data Governance & Management: Strategies for collecting, storing, and utilizing vast amounts of data from energy and manufacturing systems. - Ethics, Regulation, and Policy: Discussing the societal implications, regulatory frameworks, and ethical considerations of AI in these critical sectors. - Digital Transformation Strategies: Roadmaps and challenges for implementing AI across organizations. - Economic Impact & ROI: Analyzing the financial benefits and investment returns of AI adoption. - Education & Workforce Development: Addressing the skills gap and preparing the next generation of engineers.