Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration
Multifidelity and Multiscale Bayesian Framework for High-Dimensional Engineering Design and Calibration
复制标题
用于高维工程设计和校准的多保真度和多尺度贝叶斯框架
DOI：
10.1115/1.4044598
复制
发表时间：
2019
影响因子：
3.3
通讯作者：
P. Perdikaris
中科院分区：
文献类型：
--
作者：
S. Sarkar;Sudeepta Mondal;M. Joly;Matthew E. Lynch;S. Bopardikar;Ranadip Acharya;P. Perdikaris
本文提出了一种基于机器学习的多保真度建模（MFM）和信息论贝叶斯优化方法，其中相关模型之间可能存在复杂的差异。基于多模型的优化的优点，在一个单一的保真度代理，特别是在复杂的约束条件下，基准优化问题，涉及噪声数据进行了讨论。的MFM框架，通过高斯过程的不同保真度的信息源建模的基础上，增强与信息理论的主动学习策略，涉及顺序选择的最佳点在多尺度架构。该框架被证明是提高效率的实际工程问题，如压缩机转子的高维设计优化，通过实施其多尺度架构和昂贵的微观结构预测模型的校准。从多物理场系统的机器学习辅助设计的角度来看，所提出的框架的优势，在加速搜索预算约束下的最优设计条件。
This paper proposes a machine learning–based multifidelity modeling (MFM) and information-theoretic Bayesian optimization approach where the associated models can have complex discrepancies among each other. Advantages of MFM-based optimization over a single-fidelity surrogate, specifically under complex constraints, are discussed with benchmark optimization problems involving noisy data. The MFM framework, based on modeling of the varied fidelity information sources via Gaussian processes, is augmented with information-theoretic active learning strategies that involve sequential selection of optimal points in a multiscale architecture. This framework is demonstrated to exhibit improved efficiency on practical engineering problems like high-dimensional design optimization of compressor rotor via implementing its multiscale architecture and calibration of expensive microstructure prediction model. From the perspective of the machine learning–assisted design of multiphysics systems, advantages of the proposed framework have been presented with respect to accelerating the search of optimal design conditions under budget constraints.
DOI：
--
发表时间：
2018-11
期刊：
ArXiv
影响因子：
--
作者：
Jialin Song;Yuxin Chen;Yisong Yue
通讯作者：
Jialin Song;Yuxin Chen;Yisong Yue