Interpretable operator learning for inverse problems via adaptive spectral filtering: Convergence and resolution transfer
- DOI
- 10.1016/j.neunet.2026.109794
- Published
- 2027-02
- Container
- Neural Networks
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109794,
title = {Interpretable operator learning for inverse problems via adaptive spectral filtering: Convergence and resolution transfer},
author = {Hang-Cheng Dong and Pengcheng Cheng and Shuhuan Li},
year = {2027},
journal = {Neural Networks},
doi = {10.1016/j.neunet.2026.109794},
url = {https://doi.org/10.1016/j.neunet.2026.109794}
}RIS
TY - JOUR TI - Interpretable operator learning for inverse problems via adaptive spectral filtering: Convergence and resolution transfer AU - Hang-Cheng Dong AU - Pengcheng Cheng AU - Shuhuan Li PY - 2027 JO - Neural Networks DO - 10.1016/j.neunet.2026.109794 UR - https://doi.org/10.1016/j.neunet.2026.109794 ER -
APA
Dong, H., Cheng, P., & Li, S. (2027). Interpretable operator learning for inverse problems via adaptive spectral filtering: Convergence and resolution transfer. Neural Networks. https://doi.org/10.1016/j.neunet.2026.109794
Source records
- crossref · retrieved 2026-11-07T11:25:44.873Z