Results 91 to 100 of about 2,251,103 (302)
Multi-Objective BiLevel Optimization by Bayesian Optimization
AlgorithmsIn a multi-objective optimization problem, a decision maker has more than one objective to optimize. In a bilevel optimization problem, there are the following two decision-makers in a hierarchy: a leader who makes the first decision and a follower who ...
Vedat Dogan, Steven Prestwich
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machine-learning-tutorial/bayesian-optimization: v1.0.0
<h1>Bayesian Optimization Tutorial v1.0 Release</h1> <p>The stable version release for the Bayesian optimization tutorial, containing</p> <ul> <li>A short introduction to the Bayesian optimization concepts</li> ...
Andrea Santamaria Garcia, Chenran Xu
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Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy
Advanced Engineering Materials, EarlyView.Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang +6 more
wiley +1 more source
Exploration of outliers in strength–ductility relationship of dual-phase steels
Science and Technology of Advanced Materials: Methods, 2022 To overcome the trade-off relationship between tensile strength and elongation of dual-phase steels, three exploratory techniques were utilized: Bayesian optimization (BO), BoundLess Objective-free eXploration (BLOX), and one-class support vector machine
Takayuki Shiraiwa +3 more
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Multi-objective constrained Bayesian optimization for structural design
, 2022 S.689-701The planning and design of buildings and civil engineering concrete structures constitutes a complex problem subject to constraints, for instance, limit state constraints from design codes, evaluated by expensive computations such as finite ...
Mathern, A. +7 more
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A Tutorial on Bayesian Optimization
CoRR, 2018 Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations.
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FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery
Advanced Engineering Materials, EarlyView.We present FastNano Liquid, an automated small‐ and wide‐angle X‐ray scattering platform for the combined synthesis and characterization of (nano)materials. The platform is coupled to varied reactor workflows for both in situ studies of reaction kinetics and ex situ screening of synthesis conditions to support machine learning‐guided exploration ...
Pierre‐Baptiste Flandrin +16 more
wiley +1 more source
InformationThis study examines black-box hyperparameter optimization for financial retrieval-augmented generation (RAG) retrieval under limited budget constraints. Using FinQA as the primary dataset, it compares Grid Search, Random Search, and Bayesian Optimization
Yangyang Jin, Xindi Wang, Qianli Dong
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Tutorial on introduction to Bayesian optimization
<h1>Bayesian Optimization Tutorial v1.0.1 Release</h1> <p>Update citation information.</p>If you use this software, please cite it as ...
Xu, Chenran, Santamaria Garcia, Andrea
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Bayesian Optimization for Min Max Optimization
CoRR, 2020 A solution that is only reliable under favourable conditions is hardly a safe solution. Min Max Optimization is an approach that returns optima that are robust against worst case conditions. We propose algorithms that perform Min Max Optimization in a setting where the function that should be optimized is not known a priori and hence has to be learned ...
Dorina Weichert, Alexander Kister
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