... the use of Bayesian optimization (BO). ERGO tackles robust optimization problems which are characterized by sensitivity in the objective function due to stochasticity in the design space. It does this by concurrently minimizing the mean and variance of the objective in a multi-objective setting. To handle...

...Sangjoon Lee; Haris Moazam Sheikh; Dahyun D. Lim; Grace X. Gu; Philip S. Marcus A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing design-by-morphing (DbM), large Eddy simulation (LES), and Bayesian optimization...

...Zahra Zanjani Foumani; Amin Yousefpour; Mehdi Shishehbor; Ramin Bostanabad Bayesian optimization (BO) is a sequential optimization strategy that is increasingly employed in a wide range of areas such as materials design. In real-world applications, acquiring high-fidelity (HF) data through physical...

... the proposed GSA method is general enough to benefit various engineering design applications, we integrate it with multi-objective Bayesian optimization (BO) to create a sensitivity-aware design framework in accelerating the Pareto front design exploration for metal-organic framework (MOF) materials with many...

... are of limited use in problems that involve expensive black-box functions. In recent years, multi-objective Bayesian optimization has emerged as a powerful alternative; however, in many applications, these methods fail to generate a diverse and well-spread Pareto front. To address this limitation, our work...

... optimization Bayesian optimization Parametric optimization (or multiparametric programming [ 1 , 2 ]) introduced in the 1970s [ 3 ] looks at how an optimal solution changes as parameters change . Parametric optimization problems are different from traditional optimization problems because they directly...

... 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...

... simulation-based design metamodeling Bayesian optimization uncertainty modeling occupant restraint system‌ Arizona State University 10.13039/100007482 ASUB00000374 Crashworthiness optimization is an essential design aspect of modern vehicles and plays a critical role in preventing...

...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...