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Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

机译：机器学习材料物理学：代理优化和多保真算法预测相位场动态的替代方案中的沉淀形态

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Gregory H. Teichert; Krishna Garikipati;
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年度 2019
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1. Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics [J] . Teichert Gregory H., Garikipati Krishna Computer Methods in Applied Mechanics and Engineering . 2019,第FEBa1期

机译：机器学习材料物理学：替代优化和多保真算法可预测相态动力学的替代物沉淀形态
2. Applications of surrogate-assisted and multi-fidelity multi-objective optimization algorithms to simulation-based aerodynamic design [J] . Amrit Anand, Leifsson Leifur Engineering Computations . 2020,第2期

机译：代理辅助和多保真多目标优化算法在仿真的空气动力学设计中的应用
3. Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization [J] . Tao Jun, Sun Gang Aerospace science and technology . 2019,第SEPa期

机译：基于深度学习的多保真替代模型在鲁棒空气动力学设计优化中的应用
4. Physics-guided characterization and optimization of solar cells using surrogate machine learning model [C] . Zekun Ren, Felipe Oviedo, Hansong Xue, IEEE Photovoltaic Specialists Conference . 2019

机译：使用替代机器学习模型进行物理指导的太阳能电池表征和优化
5. Multi-fidelity optimization strategies using genetic algorithms and sequential kriging surrogates. [D] . Kolencherry, Nithin J. 2011

机译：使用遗传算法和顺序克里金代理的多保真度优化策略。
6. A predictive machine learning approach for microstructure optimization and materials design [O] . Ruoqian Liu, Abhishek Kumar, Zhengzhang Chen, -1

机译：一种用于微观结构优化和材料设计的预测性机器学习方法
7. Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys [O] . Anh Tran, Julien Tranchida, Tim Wildey, 2020

机译：多保真机 - 学习，具有不确定量化和贝叶斯优化材料设计：适用于三元随机合金

Machine learning materials physics: Surrogate optimization and multi-fidelity algorithms predict precipitate morphology in an alternative to phase field dynamics

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