arXiv Machine Learning
Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
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arXiv AI
Sep 10
From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
arXiv:2506.12045v2 Announce Type: replace-cross Abstract: Accurate reconstruction of latent environmental fields from sparse, indirect observations is a fundamental challenge across scientific domain...
arXiv Machine Learning
Aug 28
TRACE: Retrospective Streaming Generation of Physical Fields under Sparse Structured Sensing
TRACE is a retrospective streaming generative reconstruction framework designed to recover continuous physical fields from sparse, structured sensing streams. It performs approximate Bayesian inference in a continuous-coordinate latent space, fuses sparse off‑grid measurements with a state‑space temporal prior via Kalman‑style filtering, and refines past frames through retrospective smoothing. Experiments on active matter, ocean sound‑speed fields, and supernova simulations demonstrate that TRACE matches or surpasses existing frame‑wise generative reconstructors, offline spatiotemporal methods, and streaming data‑assimilation baselines in reconstruction quality under temporally sparse and spatially localized sensing protocols.
arXiv Machine Learning
Oct 2
Reconstruction of cosmic-ray direction and energy in radio arrays using deep ensemble graph neural networks
The paper presents a deep ensemble graph neural network (GNN) approach for reconstructing the arrival direction and energy of ultra‑high‑energy cosmic rays from voltage traces recorded by ground‑based radio detector arrays. Triggered antennas are modeled as a graph input to the GNN, and physical knowledge is embedded in both the architecture and data to improve precision while reducing training set size. The method achieves an angular resolution of 0.092° and an electromagnetic energy resolution of 16.4% on simulated data with realistic noise, and includes uncertainty estimation and robustness checks for real‑world applicability.
arXiv Machine Learning
Aug 11
NeuralDMD: Interpretable Neural Representation of Dynamics from Sparse and Noisy Measurements
arXiv:2507. 03094v2 Announce Type: replace-cross Abstract: Many challenges in scientific imaging involve solving ill-posed inverse problems, where the goal is to recover spatio-temporal fields from indirect, noisy, and highly sparse measurements - often without access to ground truth data or reliable simulators.
arXiv Machine Learning
Jun 2
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.
arXiv Machine Learning
Oct 28
Sparse Reconstruction by Direct Measurement-to-Field Generation in Function Space
Sparse Reconstruction by Direct Measurement-to-Field Generation in Function Space introduces a functional rectified flow framework called Direct Measurement-to-Field Generation (DMF) that learns to generate physical fields directly from sparse measurements. Unlike traditional grid-based methods, DMF jointly learns a sensor–global–sensor representation and a generative transport mechanism, enabling dynamic fusion of local grounding with global context to evaluate fields at arbitrary query coordinates. Benchmarks demonstrate that DMF outperforms baselines in scenarios with unknown boundaries, missing high-resolution training data, and unmeasured quantities, while its stochastic ensemble generation provides reliable uncertainty quantification and improved continuous ranked probability scores.