Exploring ensemble simulations is increasingly important across many scientific domains. However, supporting flexible post-hoc exploration remains challenging due to the trade-off between storing the expensive raw data and flexibly adjusting visualization settings. Existing visualization surrogate models have improved this workflow, but they either operate in image space without an explicit 3D representation or rely on neural radiance fields that are computationally expensive for interactive exploration and encode all parameter-driven variations within a single implicit field. In this work, we introduce GS-Surrogate, a deformable Gaussian Splatting-based visualization surrogate for parameter-space exploration. Our method first constructs a canonical Gaussian field as a base 3D representation and adapts it through sequential parameter-conditioned deformations. By separating simulation-related variations from visualization-specific changes, this explicit formulation enables efficient and controllable adaptation to different visualization tasks, such as isosurface extraction and transfer function editing. We evaluate our framework on a range of simulation datasets, demonstrating that GS-Surrogate enables real-time and flexible exploration across both simulation and visualization parameter spaces.

翻译：在众多科学领域中，探索集成模拟的重要性日益凸显。然而，由于需要在存储昂贵的原始数据与灵活调整可视化设置之间进行权衡，支持灵活的后期探索仍具挑战性。现有的可视化替代模型虽已改善该工作流程，但它们或在图像空间中运行而缺乏显式三维表征，或依赖神经辐射场——这种方案计算成本高昂、难以支持交互式探索，且将所有参数驱动的变化编码在单一隐式场中。本文提出GS-Surrogate，一种基于可变形高斯溅射的参数空间探索可视化替代方法。该方法首先构建规范高斯场作为基础三维表征，再通过序列化的参数条件变形对其进行自适应调整。通过将模拟相关变化与特定可视化变化分离，这种显式公式能够高效且可控地适应不同可视化任务，例如等值面提取与传递函数编辑。我们在多个模拟数据集上评估了该框架，结果表明GS-Surrogate能够在模拟与可视化参数空间实现实时且灵活的探索。