The field is witnessing a significant shift towards the development of scalable and physics-based simulation platforms for complex systems, including indoor environments, building thermal dynamics, and climate models. These platforms aim to address the challenges of data quality and quantity, and provide a foundation for unleashing artificial intelligence-driven innovation. Notable advancements include the introduction of language-guided 4D world generators, thermal building data generation frameworks, and federated reinforcement learning algorithms. These innovations have the potential to revolutionize various applications, including RF sensing, building controls, and climate modeling. Noteworthy papers include: Scalable RF Simulation in Generative 4D Worlds, which introduces WaveVerse, a prompt-based framework for simulating realistic RF signals. MuFlex, a scalable platform for benchmarking and testing control strategies for multi-building flexibility coordination. FedRAIN-Lite, a federated reinforcement learning framework for improving idealised numerical weather and climate models.