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52 ta ish

Ish: A micromechanical nested machine learning model for characterizing materials behaviors of bulk metallic glasses

  1. Bayesian machine learning-aided approach bridges between dynamic elasticity and compressive strength in the cement-based mortars

    Ning Wang, Majid Samavatian, Vahid Samavatian +1

    Maqola20237 iqtibos
    ABI

  2. The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling

    Yile Ao, Hongqi Li, Liping Zhu +2

    Maqola20186 iqtibos
    ABI

  3. Correlation-driven machine learning for accelerated reliability assessment of solder joints in electronics

    Vahid Samavatian, Mahmud Fotuhi‐Firuzabad, Majid Samavatian +2

    Maqola20205 iqtibos
    ABI

  4. Characterization of nanoscale structural heterogeneity in metallic glasses: A machine learning study

    Majid Samavatian, Reza Gholamipour, Dmitry Olegovich Bokov +3

    Maqola20213 iqtibos
    ABI

  5. Machine learning prediction of elastic properties and glass-forming ability of bulk metallic glasses

    Jie Xiong, Tong‐Yi Zhang, San‐Qiang Shi

    Maqola20192 iqtibos
    ABI

  6. Correlative study between elastic modulus and glass formation in ZrCuAl(X) amorphous system using a machine learning approach

    Jianqing Li, Tzu-Chia Chen, Angelina Olegovna Zekiy

    Maqola20212 iqtibos
    ABI

  7. Prediction of Vickers hardness of amorphous alloys based on interpretable machine learning

    Xiaowei Liu, Zhilin Long, Peng Li

    Maqola20222 iqtibos
    ABI

  8. Machine learning modeling for the prediction of plastic properties in metallic glasses

    Nicolás Amigó, Simón Palominos, Felipe J. Valencia

    Maqola20232 iqtibos
    ABI

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