Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy.
- DOI
- 10.3390/ma19132824
- Published
- 2026 Jul 2
- Container
- Materials (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/ma19132824,
title = {Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy.},
author = {Li J and Li L and Wang Z and Li C and Wang S and Kang K},
year = {2026},
journal = {Materials (Basel, Switzerland)},
doi = {10.3390/ma19132824},
url = {https://doi.org/10.3390/ma19132824}
}RIS
TY - JOUR TI - Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy. AU - Li J AU - Li L AU - Wang Z AU - Li C AU - Wang S AU - Kang K PY - 2026 JO - Materials (Basel, Switzerland) DO - 10.3390/ma19132824 UR - https://doi.org/10.3390/ma19132824 ER -
APA
J, L., L, L., Z, W., C, L., S, W., & K, K. (2026). Coupled Bayesian Identification of Residual Stress and Fracture Strength in Thin-Film Fragmentation: A Physics-Informed Neural Network Framework with Synthetic Validation of Interface Adhesion Energy.. Materials (Basel, Switzerland). https://doi.org/10.3390/ma19132824
Source records
- pubmed · retrieved 2026-09-25T08:59:00.578Z