让科学数据生成可双向、可概率化，支持科学家主动探索假设。
DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data
- 用扩散模型实现时间跨向的任意方向生成，捕捉多种可能演化路径。
- 在5个科学数据集上验证，预测准确且概率分布质量高。
- 结合交互可视化，适合需要探索假设的科研人员使用。
建模时间演化对分析和推理科学现象至关重要，但多数机器学习方法仅提供确定性前向预测，忽略多种合理结果，且极少支持逆向推理，限制了其在实际科研流程中的应用。我们提出一个融合扩散生成建模与交互式可视化分析的框架。引入DiffUNet^2，一种条件扩散模型，支持任意时间点间的双向生成，并捕捉系统演化的可能分布。基于该模型，我们的交互系统支持分支时间线探索、用户引导的状态编辑及概率空间导航，使科学家能主动探索替代假设，而非被动观察预测结果。我们在5个不同科学领域的数据集上评估模型，验证其预测精度与概率集合质量。与领域专家合作，证明该方法在支持实际科学时序数据分析流程中的有效性。通过融合建模与交互，本方法将生成模型转化为驱动假设的科研分析工具。
原文摘要 · Abstract (English)
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.
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