Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow.
- DOI
- 10.1115/1.4070545
- Published
- 2026 Apr 1
- Container
- Journal of computational and nonlinear dynamics
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- 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.
- supportingOpen access status: Normalized open-access status: open.
- 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.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1115/1.4070545,
title = {Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow.},
author = {Lu T and Shahadat MRB and Liu Q and He R and Jiang X and Li Z},
year = {2026},
journal = {Journal of computational and nonlinear dynamics},
doi = {10.1115/1.4070545},
url = {https://doi.org/10.1115/1.4070545}
}RIS
TY - JOUR TI - Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow. AU - Lu T AU - Shahadat MRB AU - Liu Q AU - He R AU - Jiang X AU - Li Z PY - 2026 JO - Journal of computational and nonlinear dynamics DO - 10.1115/1.4070545 UR - https://doi.org/10.1115/1.4070545 ER -
APA
T, L., MRB, S., Q, L., R, H., X, J., & Z, L. (2026). Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow.. Journal of computational and nonlinear dynamics. https://doi.org/10.1115/1.4070545
Source records
- pubmed · retrieved 2026-09-25T22:49:21.139Z