Topics include, but are not limited to:

• AI for Scientific and Engineering Innovation: Scientific machine learning; physics-informed and physics-guided AI; AI-enabled scientific computing and engineering simulation; digital twins; and AI-enabled scientific discovery.

• Embodied AI: Robot learning, planning, and control; reinforcement learning for embodied agents; autonomous and embodied multi-agent systems; multimodal embodied perception and interaction; and human–robot interaction.

• Spatiotemporal Intelligence: Spatiotemporal representation learning, data mining, modeling, forecasting, and decision-making for multi-source spatiotemporal data integration; intelligent urban, infrastructure, energy, and environmental systems; and AI applications in remote sensing, climate science, and Earth system sciences.

• Quantum Intelligence: Quantum machine learning; hybrid classical–quantum AI; quantum-enhanced learning and optimization; AI for quantum science and engineering; and quantum computing for AI.

Within these focus areas, the journal prioritizes original research that makes a substantive theoretical or methodological advance, establishes a broadly transferable capability or a reusable research resource, or produces important scientific or engineering insight supported by rigorous validation. It also welcomes research that advances trustworthy and explainable AI.

The journal provides an international forum for research connecting advances in AI with important scientific and engineering problems. It publishes both fundamental and applied work and is committed to rigorous, fair, and timely peer review overseen by an international editorial board.