Physics-Informed Neural Networks for Depth-Dependent Constitutive Relationships of Gradient Nanostructured 316L Stainless Steel.

Li H, Cheng Y, Wang Z, Wang X.

DOI: 10.3390/ma18153532
Published: 2025-07-28
Container: Materials (Basel)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/ma18153532,
  title = {Physics-Informed Neural Networks for Depth-Dependent Constitutive Relationships of Gradient Nanostructured 316L Stainless Steel.},
  author = {Li H and  Cheng Y and  Wang Z and  Wang X.},
  year = {2025},
  journal = {Materials (Basel)},
  doi = {10.3390/ma18153532},
  url = {https://doi.org/10.3390/ma18153532}
}

RIS

TY  - JOUR
TI  - Physics-Informed Neural Networks for Depth-Dependent Constitutive Relationships of Gradient Nanostructured 316L Stainless Steel.
AU  - Li H
AU  -  Cheng Y
AU  -  Wang Z
AU  -  Wang X.
PY  - 2025
JO  - Materials (Basel)
DO  - 10.3390/ma18153532
UR  - https://doi.org/10.3390/ma18153532
ER  -

APA

H, L., Y, C., Z, W., & X., W. (2025). Physics-Informed Neural Networks for Depth-Dependent Constitutive Relationships of Gradient Nanostructured 316L Stainless Steel.. Materials (Basel). https://doi.org/10.3390/ma18153532

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europe-pmc · retrieved 2026-09-27T03:38:11.564Z