Component design optimisation based on artificial intelligence in support of additive manufacturing repair and restoration: Current status and future outlook for …
N Abd Aziz, NAA Adnan, D Abd Wahab… - Journal of Cleaner …, 2021 - Elsevier
Abstract The Circular Economy concept aims to ensure environmental sustainability through
the recovery of durable products that have reached the end of their useable life. Recovery …
the recovery of durable products that have reached the end of their useable life. Recovery …
Digital twins for materials
Digital twins are emerging as powerful tools for supporting innovation as well as optimizing
the in-service performance of a broad range of complex physical machines, devices, and …
the in-service performance of a broad range of complex physical machines, devices, and …
Bayesian optimization with active learning of design constraints using an entropy-based approach
The design of alloys for use in gas turbine engine blades is a complex task that involves
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
balancing multiple objectives and constraints. Candidate alloys must be ductile at room …
[图书][B] Bayesian optimization and data science
F Archetti, A Candelieri - 2019 - Springer
Bayesian Optimization and Data Science Page 1 123 SPRINGER BRIEFS IN
OPTIMIZATION Francesco Archetti Antonio Candelieri Bayesian Optimization and Data …
OPTIMIZATION Francesco Archetti Antonio Candelieri Bayesian Optimization and Data …
Multi-objective materials bayesian optimization with active learning of design constraints: Design of ductile refractory multi-principal-element alloys
Bayesian Optimization (BO) has emerged as a powerful framework to efficiently explore and
exploit materials design spaces. To date, most BO approaches to materials design have …
exploit materials design spaces. To date, most BO approaches to materials design have …
Surrogate modeling: tricks that endured the test of time and some recent developments
Tasks such as analysis, design optimization, and uncertainty quantification can be
computationally expensive. Surrogate modeling is often the tool of choice for reducing the …
computationally expensive. Surrogate modeling is often the tool of choice for reducing the …
[PDF][PDF] 变可信度近似模型及其在复杂装备优化设计中的应用研究进展
周奇， 杨扬， 宋学官， 韩忠华， 程远胜， 胡杰翔… - 机械工程 …, 2020 - scholar.archive.org
变可信度近似模型通过融合不同精度分析模型的数据, 可有效平衡近似模型预测性能和建模成本
之间的矛盾, 在复杂装备优化设计中受到广泛的关注. 综述变可信度近似模型及其在复杂装备 …
之间的矛盾, 在复杂装备优化设计中受到广泛的关注. 综述变可信度近似模型及其在复杂装备 …
sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications
Bayesian optimization (BO) is an efiective surrogate-based method that has been widely
used to optimize simulation-based applications. While the traditional Bayesian optimization …
used to optimize simulation-based applications. While the traditional Bayesian optimization …
Bayesian optimization objective-based experimental design
M Imani, SF Ghoreishi - 2020 American control conference …, 2020 - ieeexplore.ieee.org
Design has become a salient part of most of the scientific and engineering tasks, embracing
a wide range of domains including real experimental settings (eg, material discovery or drug …
a wide range of domains including real experimental settings (eg, material discovery or drug …
Adaptive active subspace-based efficient multifidelity materials design
D Khatamsaz, A Molkeri, R Couperthwaite, J James… - Materials & Design, 2021 - Elsevier
Materials design calls for an optimal exploration and exploitation of the process-structure-
property (PSP) relationships to produce materials with targeted properties. Recently, we …
property (PSP) relationships to produce materials with targeted properties. Recently, we …