Papers
2
Total Citations
18
H-Index
2
About
Mihai Stoica’s research bridges the frontiers of materials science and artificial intelligence, with a focus on bulk metallic glasses and neural network optimization. His most cited work, “Elastostatic reversibility in thermally formed bulk metallic glasses” (2017, 14 citations), explores the remarkable shaping ability of these polymer-like multicomponent alloys. Stoica demonstrates how thermoplastic forming can enable the creation of multi-length scale features in minutes—a breakthrough with potential to revolutionize commercial manufacturing. This study employs nanobeam diffraction fluctuation electron microscopy to reveal elastostatic reversibility, offering fundamental insights into the structural behavior of metallic glasses under thermal and mechanical stress. In parallel, Stoica contributes to machine learning with his 2010 paper on neural network training, which proposes a method to measure training time based on the number of steps. This work supports robot programming by demonstration, where efficient learning from data is critical. Together, these contributions highlight Stoica’s versatility: he advances both the physical understanding of advanced alloys and the algorithmic efficiency of artificial neural networks. His research is valuable for students and researchers interested in the intersection of materials engineering and computational intelligence, demonstrating how fundamental science and applied AI can converge to solve complex, real-world problems.
Research Focus
Key Achievements
Top Papers
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