Results 61 to 70 of about 56,129 (265)
Bayesian inference with optimal maps [PDF]
Journal of Computational Physics, 2012 66 pages, 26 figures. Minor revisions and improvements throughout.
Tarek A. El-Moselhy, Youssef M. Marzouk
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Advanced Engineering Materials, EarlyView.Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt +8 more
wiley +1 more source
Компютерні системи та інформаційні технологіїThe performance of machine learning models depends on the selection and tuning of hyperparameters. As a widely used gradient boosting method, XGBoost relies on optimal hyperparameter configurations to balance model complexity, prevent overfitting, and ...
Микола ЗЛОБІН +1 more
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A Workflow to Accelerate Microstructure‐Sensitive Fatigue Life Predictions
Advanced Engineering Materials, EarlyView.This study introduces a workflow to accelerate predictions of microstructure‐sensitive fatigue life. Results from frameworks with varying levels of simplification are benchmarked against published reference results. The analysis reveals a trade‐off between accuracy and model complexity, offering researchers a practical guide for selecting the optimal ...
Luca Loiodice +2 more
wiley +1 more source
Data-efficient optimization of thermally-activated polymer actuators through machine learning
Materials & DesignFor applications in soft robotics and smart textiles, thermally-activated, twisted, and coiled polymer actuators can offer high mechanical actuation with proper optimization of their processing conditions. However, optimization is often aggravated by the
Yuhao Zhang +10 more
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MOBOpt — multi-objective Bayesian optimization
SoftwareX, 2020 This work presents a new software, programmed as a Python class, that implements a multi-objective Bayesian optimization algorithm. The proposed method is able to calculate the Pareto front approximation of optimization problems with fewer objective ...
Paulo Paneque Galuzio +3 more
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Applied Sciences, 2022 The 3D body scan technology has recently innovated the way of measuring human bodies and generated a large volume of body measurements. However, one inherent issue that plagues the use of the resultant database is the missing data usually caused by using
Xuebo Liu, Yingying Wu, Hongyu Wu
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Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones
Advanced Engineering Materials, EarlyView.A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell +3 more
wiley +1 more source
Optimizing Bayesian optimization
, 2018 We are concerned primarily with improving the practical applicability of Bayesian optimization. We make contributions in three key areas. We develop an intuitive online stopping criterion, allowing only as many steps as necessary to achieve the desired target to be taken. By combining this with intelligent online switching between acquisition functions
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Advanced Engineering Materials, EarlyView.We develop a data‐driven method to derive the mathematical expressions of the Flory–Huggins interaction parameter χ for the swelling behavior of temperature–responsive hydrogels. Starting from initial assumptions of χ, our workflow combines Bayesian optimization, Flory–Rehner theory, and symbolic regression to generate candidate χ expressions.
Yawen Wang +2 more
wiley +1 more source