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 …

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 …

Bayesian optimization (BO) is an efiective surrogate-based method that has been widely
used to optimize simulation-based applications. While the traditional Bayesian optimization …

Partially-observed Boolean dynamical systems (POBDS) are a general class of nonlinear
state-space models that provide a rich framework for modeling many complex dynamical …

Stabilization of complex cyber-physical systems is extremely important in keeping the critical
infrastructure and the environment safe. This is, in particular, critical in coupled …

Multi-fidelity (MF) metamodel has attracted significant attention recently in simulation-based
design and optimization. It can achieve a desirable modeling accuracy with relatively lower …

Multi-objective optimization is often a difficult task owing to the need to balance competing
objectives. A typical approach to handling this is to estimate a Pareto frontier in objective …

A promising alternative to the traditional objectives-and-constraints formulation for
optimization that uses multiple dominance relations has recently been developed. Optimal …

Searching for the optimal values of hyperparameters of a Machine Learning algorithm can
be an extremely computationally expensive and awfully energivorous process. This paper …

Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of
a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of …