Physics-informed machine learning assisted uncertainty quantification for the corrosion of dissimilar material joints
Parth Bansal(University of Illinois Urbana-Champaign), Yumeng Li(Changchun University of Science and Technology), Chenhui Shao(University of Illinois Urbana-Champaign), Blair E. Carlson(General Motors (United States)), Zhuoyuan Zheng(Nanjing Tech University), Mihaela Banu(University of Michigan), Jingjing Li(North University of China)
Cited by 36
Related Papers
Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review
|Sustainability|2018|174
In-situ reconstructed Cu/Cu2O heterogeneous nanorods with oxygen vacancies for enhanced electrocatalytic nitrate reduction to ammonia
|Chemical Engineering Journal|2023|108
Opportunities and Challenges in Metal Forming for Lightweighting: Review and Future Work
|Journal of Manufacturing Science and Engineering|2020|79
Novel 3D printed TPMS scaffolds: microstructure, characteristics and applications in bone regeneration
|Journal of Tissue Engineering|2024|60