AI, Data Analytics & Integrated Modeling in Resource Systems

Academic Scope

This sub-session focuses on advanced analytical methods and integrated modeling approaches for complex resource systems. The increasing availability of large-scale datasets and computational capabilities has accelerated the adoption of artificial intelligence, machine learning, and hybrid modeling frameworks across mineral resource applications.
The sub-session welcomes contributions related to deep learning, big data analytics, pattern recognition, data fusion, multi-sensor integration, and physics-informed AI models. Studies utilizing convolutional neural networks (CNNs), recurrent neural networks (LSTM), transformer architectures, and hybrid multi-physics modeling approaches are particularly encouraged.
The session aims to explore how advanced computational intelligence and integrated analytical frameworks can improve prediction accuracy, system understanding, and decision-making in mineral resource exploration, extraction, and management.