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

• URL: http://arxiv.org/abs/2606.03926v1
• Date: Tue, 02 Jun 2026 17:15:01 GMT
• Title: DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data
• Abstract summary: temporal evolution is important to analyzing and reasoning about scientific phenomena.<n>Most machine learning methods provide deterministic forward predictions that overlook multiple plausible outcomes.<n>We present a framework that integrates diffusion-based generative modeling with interactive visual analytics for scientific exploration.
• Score: 25.698658042998073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• From Observation to Insight: Mechanistic World Models and the Quest for Autonomous Discovery [60.11725132433485]
  We argue that scientific discovery is fundamentally a problem of knowledge organisation.<n>We introduce Mechanistic World Models, a new design paradigm that places reusable mechanisms at the centre of representation, computation and learning.
  arXiv  Detail & Related papers  (2026-07-14T08:03:27Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics [82.55776608452017]
  Large language models (LLMs) provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics.<n>This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle.<n>We identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents.
  arXiv  Detail & Related papers  (2025-10-10T22:26:26Z)
• A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers [251.23085679210206]
  Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is represented, integrated, and applied in scientific research.<n>This survey reframes the development of Sci-LLMs as a co-evolution between models and their underlying data substrate.<n>We formulate a unified taxonomy of scientific data and a hierarchical model of scientific knowledge.
  arXiv  Detail & Related papers  (2025-08-28T18:30:52Z)
• Dynamic Diffusion Schrödinger Bridge in Astrophysical Observational Inversions [33.37613202999323]
  We study Diffusion Schr"odinger Bridge (DSB) models in the context of dynamical astrophysical systems.<n>We introduce the Astro-DSB model, a variant of DSB with the pairwise domain assumption tailored for astrophysical dynamics.
  arXiv  Detail & Related papers  (2025-06-09T12:35:36Z)
• Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems [24.014942930679855]
  We introduce an encoder-only approach to learn the evolution operators of non-scale dynamical systems.<n>We show it across multiple scientific domains: explaining the folding dynamics of small proteins in host sites, and autonomously finding patterns in climate data.
  arXiv  Detail & Related papers  (2025-05-24T12:18:19Z)
• Temporal receptive field in dynamic graph learning: A comprehensive analysis [15.161255747900968]
  We present a comprehensive analysis of the temporal receptive field in dynamic graph learning. Our results demonstrate that appropriately chosen temporal receptive field can significantly enhance model performance. For some models, overly large windows may introduce noise and reduce accuracy.
  arXiv  Detail & Related papers  (2024-07-17T07:46:53Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Disentangled Neural Relational Inference for Interpretable Motion Prediction [38.40799770648501]
  We develop a variational auto-encoder framework that integrates graph-based representations and timesequence models. Our model infers dynamic interaction graphs augmented with interpretable edge features that characterize the interactions. We validate our approach through extensive experiments on both simulated and real-world datasets.
  arXiv  Detail & Related papers  (2024-01-07T22:49:24Z)
• Causal Discovery in Physical Systems from Videos [123.79211190669821]
  Causal discovery is at the core of human cognition. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure.
  arXiv  Detail & Related papers  (2020-07-01T17:29:57Z)
• EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning [41.42230144157259]
  We propose a generic trajectory forecasting framework with explicit relational structure recognition and prediction via latent interaction graphs. Considering the uncertainty of future behaviors, the model is designed to provide multi-modal prediction hypotheses. We introduce a double-stage training pipeline which not only improves training efficiency and accelerates convergence, but also enhances model performance.
  arXiv  Detail & Related papers  (2020-03-31T02:49:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.