[DMANET] [Call for short abstract] Springer_book:Generative AI and Mathematical Optimization for Green Hydrogen Systems: Operational Trends and Technical Challenges

Dear Colleague, We are editing a Springer book entitled “*Generative AI and Mathematical Optimization for Green Hydrogen Systems: Operational Trends and Technical Challenges*”. This volume aims to bring together mathematical optimization, generative AI, and data-driven methods applied to green hydrogen systems and solar-fed energy infrastructure. It combines cutting-edge computational contributions with real-world case studies, addressing operational trends, system integration, and extreme-environment deployment challenges. We cordially invite you to send your short abstract for approval. While the full chapter will be due later this year, for now, we only require the following: - Author list - Chapter title - Abstract (between 2 and 6 sentences) The deadline to submit your short abstract directly to lahby@ieee.org is *November 02, 2026* (firm deadline). The tentative structure of the book includes, but is not limited to, the following * Part I – Green Hydrogen Systems: Technologies, Value Chains, and Digital Architecture * - Green Hydrogen Systems: Technologies, Value Chains, and Sector Coupling - Electrolyzers, Energy Storage, and Hydrogen Infrastructure: Technologies and Dynamic Performance - Green Hydrogen System Modeling: Renewable Resources, Loads, Components, and System Dynamics - Digital and Cyber-Physical Architecture for Intelligent Green Hydrogen Systems *Part II – Mathematical Optimization, Decision-Making, and Intelligent Control* - Mathematical Optimization for Green Hydrogen System Design and Capacity Planning -Multi-Objective Optimization and Multi-Criteria Decision-Making for Green Hydrogen Systems - Stochastic, Robust, and Uncertainty-Aware Optimization of Renewable Hydrogen Systems - Intelligent Energy Management, Model Predictive Control, and Degradation-Aware Optimization *Part III – Generative AI and Foundation Models for Green Hydrogen Systems * -Generative AI for Renewable Energy Forecasting, Scenario Generation, and Uncertainty Modeling - Physics-Informed Generative AI, Surrogate Models, and Neural Operators for Hydrogen Systems - Generative AI for Materials, Catalysts, Electrolyzers, and Hydrogen Technology Discovery - Large Language Models, AI Agents, and Generative Digital Twins for Hydrogen Operations *Part IV – Sustainable, Resilient, and Large-Scale Green Hydrogen Deployment * - Green Hydrogen in Arid, Desert, and Harsh Environments - Integrated Water–Energy–Hydrogen Optimization for Sustainable Hydrogen Production - Large-Scale Green Hydrogen Hubs, Power-to-X, and Global Deployment Strategies Part V – Responsible, Trustworthy, and Autonomous Green Hydrogen Systems - Responsible and Sustainable Generative AI for Green Hydrogen - Trustworthy AI, Cybersecurity, Safety, and Human Oversight in Hydrogen Infrastructure - Future Directions: Toward AI-Native, Autonomous, Resilient, and Self-Optimizing Hydrogen Systems NB. There is ne fee for publication and the book will be indexed by scopus Best regards -- *Dr. M.Lahby* Laboratory of Mathematics and Applications, University Hassan II, Ecole Normale Supérieure (ENS) Casablanca, Morocco mlahby@gmail.com GSM : +212 6 65 29 23 76 In the world of Linux, who needs Windows and Gates ********************************************************** * * Contributions to be spread via DMANET are submitted to * * DMANET@zpr.uni-koeln.de * * Replies to a message carried on DMANET should NOT be * addressed to DMANET but to the original sender. The * original sender, however, is invited to prepare an * update of the replies received and to communicate it * via DMANET. * * DISCRETE MATHEMATICS AND ALGORITHMS NETWORK (DMANET) * http://www.zaik.uni-koeln.de/AFS/publications/dmanet/ * **********************************************************