Machine Learning-Based Surrogate Modelling for Efficient Inverse Analysis of Micro-Indentation Response to Determine Material Parameters.

Sajjad S, Knorr S, Schellenberg D, Chudoba T, Clausner A, Hartmaier A

DOI: 10.3390/ma19122435
Published: 2026 Jun 7
Container: Materials (Basel, Switzerland)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/ma19122435,
  title = {Machine Learning-Based Surrogate Modelling for Efficient Inverse Analysis of Micro-Indentation Response to Determine Material Parameters.},
  author = {Sajjad S and Knorr S and Schellenberg D and Chudoba T and Clausner A and Hartmaier A},
  year = {2026},
  journal = {Materials (Basel, Switzerland)},
  doi = {10.3390/ma19122435},
  url = {https://doi.org/10.3390/ma19122435}
}

RIS

TY  - JOUR
TI  - Machine Learning-Based Surrogate Modelling for Efficient Inverse Analysis of Micro-Indentation Response to Determine Material Parameters.
AU  - Sajjad S
AU  - Knorr S
AU  - Schellenberg D
AU  - Chudoba T
AU  - Clausner A
AU  - Hartmaier A
PY  - 2026
JO  - Materials (Basel, Switzerland)
DO  - 10.3390/ma19122435
UR  - https://doi.org/10.3390/ma19122435
ER  -

APA

S, S., S, K., D, S., T, C., A, C., & A, H. (2026). Machine Learning-Based Surrogate Modelling for Efficient Inverse Analysis of Micro-Indentation Response to Determine Material Parameters.. Materials (Basel, Switzerland). https://doi.org/10.3390/ma19122435

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pubmed · retrieved 2026-09-25T15:32:21.755Z