Peer-reviewedLicence not statedInternational Journal of Plasticity
A predictive transferability index for crystal plasticity framework selection: Variational derivation, analytical bounds, and FFT-based plausibility assessment
Related
- Cross-exposure transferability and failure boundaries of self-supervised DNN denoising for AR-HAXPES depth profiling
- Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules
- Molecular Transferability of a Noble-Gas Coordinate for Electronegativity Equalization
- Validation-Aware VNIR Spectroscopy Reveals Target-Specific Transferability of SOC, SIC, and Gypsum Predictions in Dryland Agricultural Soils
- Fine-tuning MLIP foundation models: strategies for accuracy and transferability
BibTeX
@article{jin2026predictive,
title = {{A predictive transferability index for crystal plasticity framework selection: Variational derivation, analytical bounds, and FFT-based plausibility assessment}},
author = {Jianqiang Jin and Balaji Raghavan and Ludovic Cauvin and Manyu Xiao and Emina Hadzalic and Piotr Breitkopf and Salima Bouvier},
journal = {International Journal of Plasticity},
year = {2026},
month = oct,
doi = {10.1016/j.ijplas.2026.104838},
url = {https://utc.hal.science/hal-05843448v1},
}
RIS
TY - JOUR
TI - A predictive transferability index for crystal plasticity framework selection: Variational derivation, analytical bounds, and FFT-based plausibility assessment
AU - Jianqiang Jin
AU - Balaji Raghavan
AU - Ludovic Cauvin
AU - Manyu Xiao
AU - Emina Hadzalic
AU - Piotr Breitkopf
AU - Salima Bouvier
PY - 2026
DA - 2026/10/29
JO - International Journal of Plasticity
DO - 10.1016/j.ijplas.2026.104838
UR - https://utc.hal.science/hal-05843448v1
SN - 0749-6419
ER -