SedNet: A physics-informed operator-learning framework for rapid sedimentation velocity analytical ultracentrifugation analysis
- DOI
- 10.1016/j.ijpharm.2026.126972
- Published
- 2026-06
- Container
- International Journal of Pharmaceutics
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.ijpharm.2026.126972,
title = {SedNet: A physics-informed operator-learning framework for rapid sedimentation velocity analytical ultracentrifugation analysis},
author = {Andrew Basalla and Krishna Kumar and Zhengrong Cui and Robert O. Williams},
year = {2026},
journal = {International Journal of Pharmaceutics},
doi = {10.1016/j.ijpharm.2026.126972},
url = {https://doi.org/10.1016/j.ijpharm.2026.126972}
}RIS
TY - JOUR TI - SedNet: A physics-informed operator-learning framework for rapid sedimentation velocity analytical ultracentrifugation analysis AU - Andrew Basalla AU - Krishna Kumar AU - Zhengrong Cui AU - Robert O. Williams PY - 2026 JO - International Journal of Pharmaceutics DO - 10.1016/j.ijpharm.2026.126972 UR - https://doi.org/10.1016/j.ijpharm.2026.126972 ER -
APA
Basalla, A., Kumar, K., Cui, Z., & Williams, R. O. (2026). SedNet: A physics-informed operator-learning framework for rapid sedimentation velocity analytical ultracentrifugation analysis. International Journal of Pharmaceutics. https://doi.org/10.1016/j.ijpharm.2026.126972
Source records
- crossref · retrieved 2026-09-26T04:29:05.687Z