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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:2601. 12614v4 Announce Type: replace-cross Abstract: Hybrid-Vlasov simulations resolve ion-kinetic effects in the solar wind-magnetosphere interaction, but even 5D (2D + 3V) configurations are computationally expensive.
arXiv:2606. 17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget.
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.
The paper presents a method for single‑image super‑resolution of solar magnetograms, converting low‑resolution SOHO/MDI data into high‑resolution SDO/HMI line‑of‑sight images. It uses a modified RRDBNet architecture initialized with ESRGAN weights and introduces an adaptive stratified specialist ensemble (SSE) that trains three specialist networks on different image complexity strata, guided by a lightweight router and uncertainty estimation. Experiments show the ensemble outperforms related approaches, improving reconstruction quality across heterogeneous space‑based instruments.
The paper introduces Physics-Unrolled Hybrid Neural Operator (PU‑HNO), a three‑stage cascade that transforms low‑fidelity ray‑tracing outputs and scene priors into high‑fidelity indoor radio maps by sequentially modeling reflection, diffraction, and scattering. It demonstrates that, under conditionally unbiased label noise, the model can learn stable propagation structures and surpass its own training labels. Experiments on varied floorplans show PU‑HNO outperforming image‑to‑image baselines, wireless learning models, and monolithic neural operators in both image quality and wireless deployment metrics.