Machine Learning-Augmented Optimization Methods for Energy and Infrastructure Systems

Dear Colleagues,

Energy systems and critical infrastructure networks—such as electric power grids, transportation networks, computing infrastructure, water systems, and communications infrastructure—are undergoing rapid transformation driven by electrification, digitalization, and globalization. Planning and optimizing these systems require solving large-scale, high-dimensional, and highly constrained optimization problems under deep uncertainty, complex interdependencies, and evolving operational conditions.

Recent advances in machine learning (ML) provide powerful new capabilities to augment classical optimization and operations research methods. ML can accelerate solvers, learn surrogate models, improve uncertainty representation, guide search processes, and enable adaptive, data-driven planning. This Special Issue focuses on learning-augmented optimization frameworks that integrate ML with mathematical programming, network optimization, and decision analysis to advance energy and infrastructure planning, design, and operations.

Dr. Olufemi A. Omitaomu
Dr. Chen Yang
Dr. Haoran Niu
Guest Editors

Manuscript Submission Information

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• machine learning-augmented optimization
• learning-augmented algorithms
• hybrid AI-optimization methods
• energy system optimization
• reinforcement learning for planning
• critical infrastructure planning
• robust and stochastic optimization
• spatial and network optimization
• digital twins for infrastructure systems
• explainable AI for decision support