DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data

2026-06-02

AI Summary

This work addresses the limitations of conventional machine learning approaches, which typically rely on deterministic forward prediction and thus struggle to model multiple plausible outcomes or support backward inference—capabilities essential for scientific workflows. To overcome this, we propose a novel system integrating a conditional diffusion model (DiffUNet²) with interactive visual analytics, enabling, for the first time, bidirectional probabilistic generation at arbitrary time points in scientific time-series data. Our approach facilitates exploration of branching timelines, state editing, and navigation within probability spaces, effectively transforming generative models into user-guided, hypothesis-driven discovery tools. We demonstrate the method’s efficacy through evaluations on five cross-domain scientific datasets, showing high predictive accuracy and high-quality probabilistic ensembles, and validate its practical utility in real-world scientific analysis through expert collaboration.

Abstract

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.

Problem

• temporal evolution
• scientific data
• bidirectional reasoning
• probabilistic modeling
• hypothesis exploration

Innovation

• diffusion model
• bidirectional generation
• probabilistic modeling
• interactive visual analytics
• scientific data exploration