Earth System World Model for What-If Simulations: A Case Study for Terrestrial Ecosystems
Machine learning emulators have become essential for accelerating expensive Earth-system simulations, but most existing approaches remain passive forecasters: they reproduce simulator trajectories under prescribed forcings without an explicit interaction mechanism for user-specified interventions. This limits their use in interactive scientific workflows and Earth-system digital twins, where users often need to explore how a system would respond if selected state components were changed. We propose an action-conditioned world-modeling framework for Earth-system emulation that reformulates simulator trajectories as supervision for controllable state-transition learning. The key idea is tra...
| Authors | Zhihao Wang, Ruichen Wang, Ruohan Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 8 September 2026 |
| Last updated at source | 8 September 2026 |
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