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Significance of artificial neural network analytical models in materials’ performance prediction

Phyu Hnin Thike, Zhaoyang Zhao, Peng Shi, Ying Jin

2020Bulletin of Materials Science64 citationsDOI

Topics & Concepts

Artificial neural networkContext (archaeology)GeneralizationComputer sciencePredictive modellingRanking (information retrieval)Noise (video)Machine learningArtificial intelligenceData miningMathematicsPaleontologyBiologyMathematical analysisImage (mathematics)Machine Learning in Materials ScienceHydrogen embrittlement and corrosion behaviors in metalsCorrosion Behavior and Inhibition
Significance of artificial neural network analytical models in materials’ performance prediction | Litcius