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learning algorithm » learning algorithms (Expand Search)
method algorithm » network algorithm (Expand Search), means algorithm (Expand Search), mean algorithm (Expand Search)
elements method » element method (Expand Search)
code algorithm » cosine algorithm (Expand Search), novel algorithm (Expand Search), modbo algorithm (Expand Search)
a learning » _ learning (Expand Search), e learning (Expand Search), q learning (Expand Search)
data code » data model (Expand Search), data came (Expand Search)
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Multi-material topology optimization using scaled boundary finite element method
Published 2025“…<p>This work introduces the use of Scaled Boundary Finite Element Method (SBFEM) in Multi-Material Topology Optimization (MMTO) problems, which is new in the literature. …”
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Data and codes for the paper "Machine-Learning-Enabled Spatial Pattern Mining: Evaluating the Impact of Imperfect Inputs".
Published 2025“…We develop soft-label versions of SPM algorithms that can directly execute on uncertain predictions generated by ML models. …”
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Fractional Cross-Validation for Optimizing Hyperparameters of Supervised Learning Algorithms
Published 2025“…In this work, we propose a highly-efficient Bayesian optimization algorithm for optimizing the hyperparameters of supervised learning algorithms with K-fold CV error as the evaluation criterion. …”
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Code and data for "A reduced-scale adaptive morphing vehicle prototype with enhanced aerodynamic efficiency"
Published 2025“…<p dir="ltr">This dataset contains:</p><ul><li>CAD files for the design and fabrication of the proposed adaptive morphing vehicle prototype;</li><li>Computer programs for genetic-algorithm-based autonomous optimization;</li><li>Raw data acquired in the wind tunnel experiments for demonstrating and validating the morphing vehicle prototype and optimization algorithm; and</li><li>Computer codes for post-processing and visualizing raw data.…”
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Overview of the C&CG method.
Published 2025“…The model is solved iteratively using the column generation algorithm and strong duality theory. Case studies on a Northeast China power grid demonstrate that, by optimally configuring generation and storage capacity guided by flexibility and other indicators, the proposed method reduces curtailment/load shedding costs and system flexibility insufficiency probability by 45% and 4.3% respectively. …”
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