发表机构
University of Maryland; Rutgers University; University of Texas at Austin; University of Illinois Urbana-Champaign（马里兰大学; 罗格斯大学; 德克萨斯大学奥斯汀分校; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出动作条件世界建模框架，将地球系统模拟器轨迹重构为可控状态转换学习，实现交互式假设情景模拟，并在全球生态系统动态上验证了高精度与可控干预能力。
AI 中文摘要
机器学习仿真器已成为加速昂贵的地球系统模拟的关键工具，但现有大多数方法仍是被动预测器：它们在规定的强迫条件下重现模拟器轨迹，缺乏针对用户指定干预的显式交互机制。这限制了它们在交互式科学工作流和地球系统数字孪生中的应用，在这些场景中，用户通常需要探索如果选定的状态组件发生变化，系统将如何响应。我们提出了一种用于地球系统仿真的动作条件世界建模框架，该框架将模拟器轨迹重新表述为可控状态转换学习的监督信号。关键思想是转换动作预训练：将自然观测到的状态变化视为无标签的动作监督，使模型无需人工标注干预即可学习规定动力学和动作条件响应。我们进一步引入掩码响应学习，以在部分状态编辑下推断未观测变量并学习耦合的系统依赖关系。我们在六个全球区域和多个林龄的生态系统动态上测试了该框架。实验表明，该模型在保持具有竞争力的长时程仿真精度的同时，能够实现可控的结构干预和耦合生态系统循环变量中的连贯响应。这些结果表明了一条从被动地球系统仿真器走向交互式、干预感知的科学替代模型的实用路径。
英文摘要
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 transition-action pretraining: naturally observed state changes are treated as label-free action supervision, allowing the model to learn both prescribed dynamics and action-conditioned responses without manually annotated interventions. We further introduce masked response learning to infer unobserved variables under partial state edits and learn coupled system dependencies. We test this framework on ecosystem dynamics across six global regions and multiple stand ages. Experiments show that the model preserves competitive long-horizon emulation accuracy while enabling controllable structural interventions and coherent responses in coupled ecosystem-cycle variables. These results suggest a practical route from passive Earth-system emulators toward interactive, intervention-aware scientific surrogates.
CommentsAccepted in SIGSPATIAL'26
