From Proxies to Fields: Spatiotemporal Reconstruction of Global Radiation from Sparse Sensor Sequences
cs.LG, cs.AI, eess.SP
Submitted: 2025-05-24
Updated: 2026-09-06
Project page: https://www.nmdb.eu/station/invk
License: http://creativecommons.org/licenses/by/4.0/
The gist: Accurate reconstruction of latent environmental fields from sparse, indirect observations is a fundamental challenge across scientific domains, from atmospheric science and geophysics to public
Terminology
Abstract
Accurate reconstruction of latent environmental fields from sparse, indirect observations is a fundamental challenge across scientific domains, from atmospheric science and geophysics to public health and aerospace safety. Existing approaches typically rely on physics-based simulations or dense sensor networks; however, these methods are hampered by high computational cost, latency, and limited spatial coverage. Here we introduce the Temporal Radiation Operator Network (TRON), a spatiotemporal neural operator architecture that infers continuous global scalar fields solely from sequences of sparse, non-uniform proxy measurements. Unlike recent prediction models that require dense, gridded inputs to predict system states, TRON tackles sparse-to-dense, cross-domain field reconstruction. It reconstructs the current global field in real time from sparse, temporally evolving sensor data, without access to any future observations or dense ground-truth fields. We demonstrate this approach on global cosmic radiation dose mapping: TRON, trained on daily reference fields spanning 2001 to 2023, generalizes across 65,341 spatial locations with input sequences ranging from 7 to 90 days. It achieves sub-second inference with relative L 2 errors below 0.1%, representing over a 58,000 times speedup compared to physics-based estimators. Although showcased in the radiation application, TRON provides a domain-agnostic framework for continuous field reconstruction from sparse data, with broad applications in atmospheric modeling, geophysical hazard monitoring, and real-time environmental risk prediction.
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