Physics-inspired feature engineering and explainable machine learning for tensile strength prediction of CNT–Si₃N₄ reinforced FWTPET joints
- DOI
- 10.3389/frai.2026.1952539
- Published
- 2026-10-20
- Container
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/frai.2026.1952539,
title = {Physics-inspired feature engineering and explainable machine learning for tensile strength prediction of CNT–Si₃N₄ reinforced FWTPET joints},
author = {Krishnan Venkatachalam and Senthil Kumaran Selvaraj and R. Lokeshkumar and G. Sundaramali},
year = {2026},
journal = {Frontiers in Artificial Intelligence},
doi = {10.3389/frai.2026.1952539},
url = {https://doi.org/10.3389/frai.2026.1952539}
}RIS
TY - JOUR TI - Physics-inspired feature engineering and explainable machine learning for tensile strength prediction of CNT–Si₃N₄ reinforced FWTPET joints AU - Krishnan Venkatachalam AU - Senthil Kumaran Selvaraj AU - R. Lokeshkumar AU - G. Sundaramali PY - 2026 JO - Frontiers in Artificial Intelligence DO - 10.3389/frai.2026.1952539 UR - https://doi.org/10.3389/frai.2026.1952539 ER -
APA
Venkatachalam, K., Selvaraj, S. K., Lokeshkumar, R., & Sundaramali, G. (2026). Physics-inspired feature engineering and explainable machine learning for tensile strength prediction of CNT–Si₃N₄ reinforced FWTPET joints. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2026.1952539
Source records
- crossref · retrieved 2026-11-10T12:41:37.975Z