Bayesian bilevel operator learning with low-rank adaptation for efficient uncertainty quantification of PDE inverse problems
- DOI
- 10.1038/s41467-026-77768-7
- Published
- 2026-09-19
- Container
- Nature Communications
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- supportingDOI registered: A matching record was returned by Crossref.
- supportingDOI resolves: A matching record was returned by Crossref.
- not scoredDirectory of Open Access Journals: No matching DOAJ record was present in this response. No allow-list match; this is not evidence of low credibility.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredOpen access status: Not checked or no result supplied; no credibility inference made.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1038/s41467-026-77768-7,
title = {Bayesian bilevel operator learning with low-rank adaptation for efficient uncertainty quantification of PDE inverse problems},
author = {Ray Zirui Zhang and Christopher E. Miles and Xiaohui Xie and John S. Lowengrub},
year = {2026},
journal = {Nature Communications},
doi = {10.1038/s41467-026-77768-7},
url = {https://doi.org/10.1038/s41467-026-77768-7}
}RIS
TY - JOUR TI - Bayesian bilevel operator learning with low-rank adaptation for efficient uncertainty quantification of PDE inverse problems AU - Ray Zirui Zhang AU - Christopher E. Miles AU - Xiaohui Xie AU - John S. Lowengrub PY - 2026 JO - Nature Communications DO - 10.1038/s41467-026-77768-7 UR - https://doi.org/10.1038/s41467-026-77768-7 ER -
APA
Zhang, R. Z., Miles, C. E., Xie, X., & Lowengrub, J. S. (2026). Bayesian bilevel operator learning with low-rank adaptation for efficient uncertainty quantification of PDE inverse problems. Nature Communications. https://doi.org/10.1038/s41467-026-77768-7
Source records
- crossref · retrieved 2026-11-10T13:22:13.200Z