18th PARIS International Conference on “Artificial Intelligence, Energy & Manufacturing Engineering”: AIEME-25

Call for papers/Topics

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.