TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing

arXiv:2608.26219 · stat.ML, cs.LG · Submitted 2026-08-26 · Read on arXiv

stat.ML, cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

Terminology

• Trace — 154× in this paper · explained in 2 episodes
• Active Matter — 50× in this paper
• Core — 19× in this paper · explained in 2 episodes
• Latent Space — 7× in this paper · explained in 7 episodes
• Diffusion Posterior Sampling — 6× in this paper
• Latent Representation — 6× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Calibration — 4× in this paper · explained in 7 episodes
• Diffusion Model — 4× in this paper · explained in 8 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Sparsity — 4× in this paper · explained in 4 episodes
• Bayesian Inference — 3× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Decode — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Empirical Coverage — 2× in this paper · explained in 2 episodes
• Error Decomposition — 2× in this paper · explained in 3 episodes
• Functional Tucker Model — 2× in this paper
• Generative Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Tucker Decomposition — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Curse of Dimensionality — 1× in this paper · explained in 6 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distribution Shift — 1× in this paper · explained in 5 episodes
• Gaussian Processes (GPs) — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Score Function — 1× in this paper · explained in 4 episodes

Sources

• Functional Complexity-adaptive Temporal Tensor Decomposition
• ForcingDAS: Unified and Robust Data Assimilation via Diffusion Forcing
• LD-EnSF: Synergizing Latent Dynamics with Ensemble Score Filters for Fast Data Assimilation with Sparse Observations

Related papers

• Behavior of prediction performance metrics with rare events
• Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs
• A Posterior-Dynamics Framework for Imaging Inverse Problems with Pretrained Diffusion Priors
• One Permutation Is All You Need: Fast, Deterministic Feature Importance and Model Stress-Testing
• Online Conformal Prediction for Non-Exchangeable Panel Data
• Deep Time-Series Forecasting in 10 Years: A Survey