... author. Email: rmalak@tamu.edu Contributed by the Design Automation Committee of ASME for publication in the J ournal of M echanical D esign . 15 06 2022 20 01 2023 23 01 2023 17 02 2023 design optimization metamodeling multi-objective optimization Bayesian...

... required to evaluate optimization objectives in materials design applications, constitute the main portion of the cost of the design process and underline the need for efficient search strategies—Bayesian optimization (BO) being one of the most widely adopted. Although recent developments in mixed-variable...

.../13588265.2011.648518 [3] Liu , K. , Wu , T. , Detwiler , D. , Panchal , J. , and Tovar , A. , 2019 , “ Design for Crashworthiness of Categorical Multimaterial Structures Using Cluster Analysis and Bayesian Optimization ,” ASME J. Mech. Des. , 141 ( 12 ), p. 121701 . 10.1115/1.4044838...

...Arpan Biswas; Claudio Fuentes; Christopher Hoyle Bayesian optimization (BO) is a low-cost global optimization tool for expensive black-box objective functions, where we learn from prior evaluated designs, update a posterior surrogate Gaussian process model, and select new designs for future...

...Arpan Biswas; Christopher Hoyle The paper presents a novel approach to applying Bayesian Optimization (BO) in predicting an unknown constraint boundary, also representing the discontinuity of an unknown function, for a feasibility check on the design space, thereby representing a classification...

... algorithms for solving such problems are heuristic methods. In this paper, a surrogate-based discrete Bayesian optimization method is developed to perform network design, where the most trustworthy CPSS network with respect to a reference node is formed to collaborate and share information...

... decomposition (EEMD), long short-term memory (LSTM) neural networks, and Bayesian optimization (BO). To improve the predictability of stochastic and nonstationary time series, the EEMD method is implemented to decompose the original time series into several components (each component is a single-frequency...

...Leshi Shu; Ping Jiang; Xinyu Shao; Yan Wang Bayesian optimization is a metamodel-based global optimization approach that can balance between exploration and exploitation. It has been widely used to solve single-objective optimization problems. In engineering design, making trade-offs between...

...Soumalya Sarkar; Sudeepta Mondal; Michael Joly; Matthew E. Lynch; Shaunak D. Bopardikar; Ranadip Acharya; Paris Perdikaris This paper proposes a machine learning–based multifidelity modeling (MFM) and information-theoretic Bayesian optimization approach where the associated models can have complex...

... generation, design clustering, and Bayesian optimization. In the first step, a conceptual design is generated using the hybrid cellular automaton (HCA) algorithm. In the second step, threshold-based cluster analysis yields a lower-dimensional design. Here, a cluster validity index for structural optimization...

... on problem metadata and refines them for the current problem using a Bayesian optimization approach. The approach is demonstrated for a simple topology optimization problem with the objective of achieving good topology optimization solution quality and then with the additional objective of finding an optimal...

...-dimensional latent variables serve as design variables, and a Bayesian optimization framework is applied to obtain microstructures with desired material property. Due to the special design of the network architecture, the proposed methodology is able to identify the latent (design) variables with desired...