pBO-2GP-3B: A batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics

Tran, Anh; Sun, Jing; Furlan, John M.; Pagalthivarthi, Krishnan V.; Visintainer, Robert; Wang, Yan

doi:10.1016/j.cma.2018.12.033

Cited by 63 publications

(26 citation statements)

References 29 publications

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“…In fact, the described extension is similar to Bayesian optimization approaches that account for unknown constraints using classification methods, see for example Sacher et al (2018), Heese et al (2019) and Tran et al (2019).…”

Section: Avoid Queries In Regions Out Of Scopementioning

confidence: 99%

Efficient Exploration of Microstructure-Property Spaces via Active Learning

et al. 2022

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In materials design, supervised learning plays an important role for optimization and inverse modeling of microstructure-property relations. To successfully apply supervised learning models, it is essential to train them on suitable data. Here, suitable means that the data covers the microstructure and property space sufficiently and, especially for optimization and inverse modeling, that the property space is explored broadly. For virtual materials design, typically data is generated by numerical simulations, which implies that data pairs can be sampled on demand at arbitrary locations in microstructure space. However, exploring the space of properties remains challenging. To tackle this problem, interactive learning techniques known as active learning can be applied. The present work is the first that investigates the applicability of the active learning strategy query-by-committee for an efficient property space exploration. Furthermore, an extension to active learning strategies is described, which prevents from exploring regions with properties out of scope (i.e., properties that are physically not meaningful or not reachable by manufacturing processes).

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Section: Avoid Queries In Regions Out Of Scopementioning

confidence: 99%

Efficient Exploration of Microstructure-Property Spaces via Active Learning

et al. 2022

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“…It approximates the optimal solution of the real problem as quickly as possible through a limited number of evaluations. Therefore, BO is now used not only for various expensive machine learning problems [31,32] but also gradually for time-consuming simulation-based mechanical optimization design problems [33,34], such as the optimization of impeller shapes [35]. After the surrogate model of the actual function is constructed using the Gaussian process as shown in Figure 5, we actively query the optimal solution of the model by minimizing the acquisition function, which is composed of the predicted mean and predicted standard deviation of the Gaussian model, as shown in Figure 6 is the expected improvement (EI) [36] acquisition function.…”

Section: Beginmentioning

confidence: 99%

Bayesian Optimization Design of Inlet Volute for Supercritical Carbon Dioxide Radial-Flow Turbine

et al. 2021

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The radial-flow turbine, a key component of the supercritical CO2 ( S-CO2) Brayton cycle, has a significant impact on the cycle efficiency. The inlet volute is an important flow component that introduces working fluid into the centripetal turbine. In-depth research on it will help improve the performance of the turbine and the entire cycle. This article aims to improve the volute flow capacity by optimizing the cross-sectional geometry of the volute, thereby improving the volute performance, both at design and non-design points. The Gaussian process surrogate model based parameter sensitivity analysis is first conducted, and then the optimization process is implemented by Bayesian optimization (BO) wherein the acquisition function is used to query optimal design. The results show that the optimized volute has better and more uniform flow characteristics at design and non-design points. It has a smoother off-design conditions performance curve. The total pressure loss coefficient at the design point of the optimized volute is reduced by 33.26%, and the flow deformation is reduced by 54.55%.

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“…The penalty function [61] is used to handle constraints in this example. For unknown or computationally expensive constraints, surrogates of constraints can also be constructed to quickly identify the infeasible solutions [23]. Each of the five approaches is run 10 times for this problem.…”

Section: An Engineeringmentioning

confidence: 99%

“…Commonly used acquisition functions include expected improvement (EI) [13,14], probability of improvement (PI) [15,16], and lower confidence bound (LCB) [17,18], where uncertainty associated with the predicted objective values from the surrogates is considered. BO has been studied for a wide range of applications, such as robotics [19,20], combinatorial optimization [21], machine learning [22], simulation-based design optimization [23,24], and others.…”

Section: Introductionmentioning

confidence: 99%

A New Multi-Objective Bayesian Optimization Formulation With the Acquisition Function for Convergence and Diversity

Shu

Jiang

Shao

et al. 2020

Journal of Mechanical Design

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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 multiple conflicting objectives is common. In this work, a multi-objective Bayesian optimization approach is proposed to obtain the Pareto solutions. A novel acquisition function is proposed to determine the next sample point, which helps improve the diversity and convergence of the Pareto solutions. The proposed approach is compared with some state-of-the-art metamodel-based multi-objective optimization approaches with four numerical examples and one engineering case. The results show that the proposed approach can obtain satisfactory Pareto solutions with significantly reduced computational cost.

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pBO-2GP-3B: A batch parallel known/unknown constrained Bayesian optimization with feasibility classification and its applications in computational fluid dynamics

Cited by 63 publications

References 29 publications

Efficient Exploration of Microstructure-Property Spaces via Active Learning

Efficient Exploration of Microstructure-Property Spaces via Active Learning

Bayesian Optimization Design of Inlet Volute for Supercritical Carbon Dioxide Radial-Flow Turbine

A New Multi-Objective Bayesian Optimization Formulation With the Acquisition Function for Convergence and Diversity

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