Multifidelity Modeling
多保真建模

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10.2514/1.J059507

The objective of multifidelity modeling is to achieve both accurate and efficient predictions by combining high- and low-fidelity models.

多保真建模的目标是通过结合高保真和低保真模型来实现准确和高效的预测。

Optimization of Information Gain in Multifidelity High-Speed Pressure Predictions

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10.1115/1.4051748

These tasks demonstrate the application of multifidelity modeling, global sensitivity analysis, intelligent design of experiments, and deep transfer learning for a meso-scale meltpool model of the additive manufacturing process.

这些任务展示了多保真建模、全局灵敏度分析、实验智能设计和深度迁移学习在增材制造过程的中尺度熔池模型中的应用。

Application of Deep Transfer Learning and Uncertainty Quantification for Process Identification in Powder Bed Fusion

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10.1016/j.cma.2018.10.025

Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modeling as an alternate framework to explore phenomena controlled by energy extremization.

在这里，我们利用机器学习方法来表示高维数据，并结合代理优化、敏感性分析和多保真建模作为替代框架来探索由能量极端化控制的现象。

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

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Multifidelity Modeling 多保真建模

Multifidelity Modeling 多保真建模
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Multifidelity Modeling(多保真建模)研究综述

Multifidelity Modeling 多保真建模 - The objective of multifidelity modeling is to achieve both accurate and efficient predictions by combining high- and low-fidelity models. [1] These tasks demonstrate the application of multifidelity modeling, global sensitivity analysis, intelligent design of experiments, and deep transfer learning for a meso-scale meltpool model of the additive manufacturing process. [2] Here, we exploit the machine learning methods to represent high-dimensional data, combined with surrogate optimization, sensitivity analysis and multifidelity modeling as an alternate framework to explore phenomena controlled by energy extremization. [3]
多保真建模的目标是通过结合高保真和低保真模型来实现准确和高效的预测。 [1] 这些任务展示了多保真建模、全局灵敏度分析、实验智能设计和深度迁移学习在增材制造过程的中尺度熔池模型中的应用。 [2] 在这里，我们利用机器学习方法来表示高维数据，并结合代理优化、敏感性分析和多保真建模作为替代框架来探索由能量极端化控制的现象。 [3]