Modeling temporal evolution is important to analyzing and reasoning about scientific phenomena, yet most machine learning methods provide deterministic forward predictions that overlook multiple plausible outcomes and rarely support backward reasoning, limiting their usefulness in practical scientific workflows. We present a framework that integrates diffusion-based generative modeling with interactive visual analytics for scientific exploration. We introduce DiffUNet^2, a conditional diffusion model that enables bidirectional, any-to-any generation across time and captures distributions of plausible system evolutions. Built upon the model, our interactive system supports branching timeline exploration, user-guided state editing, and probability-space navigation, enabling scientists to actively explore alternative hypotheses rather than passively observe predictions. We evaluate the model on 5 datasets across different scientific domains to validate its predictive accuracy and probability-space ensemble quality. In collaboration with domain experts, we demonstrate the effectiveness of our approach in supporting practical scientific temporal data analysis workflows. By integrating modeling and visual interaction, our approach enables scientists to interactively explore system dynamics, transforming generative models into tools for hypothesis-driven scientific analysis.

翻译：时间演化建模对于分析和推理科学现象至关重要，然而大多数机器学习方法仅提供确定性前向预测，忽略了多种可能的结果，且很少支持反向推理，这限制了它们在科学工作流程中的实用性。我们提出一个融合基于扩散的生成建模与交互式视觉分析的科学探索框架。我们引入DiffUNet^2，一种条件扩散模型，能够实现跨时间的双向、任意到任意生成，并捕捉系统演化的概率分布。基于该模型，我们的交互式系统支持分支时间线探索、用户引导的状态编辑以及概率空间导航，使科学家能够主动探索替代假设，而非被动观察预测。我们在5个不同科学领域的数据集上评估了该模型，以验证其预测准确性和概率空间集成质量。通过与领域专家协作，我们证明了该方法在支持实际科学时间数据分析工作流程中的有效性。通过整合建模与视觉交互，我们的方法使科学家能够交互式探索系统动力学，将生成模型转化为假设驱动的科学分析工具。