Tag: Gaussian process regression

A deep learning framework using conditional convolutional autoencoders directly on structured computational grids cut aerodynamic flow simulation time to a ...

Researchers found that formulation-level validation is essential for reliably predicting the mechanical properties of multi-fiber hybrid epoxy composites using machine ...

Machine learning and mechanistic modeling identified optimal alkaline conditions and synergistic sacrificial reactions in CuTaS3/AgTaS3 hydrogen evolution catalysts using existing ...

Machine learning models, particularly Gaussian process regression, outperform conventional statistical formulas in predicting structural punchout distress on continuously reinforced concrete ...

Researchers have demonstrated that analyzing mel-frequency cepstral coefficients from optical extinction signals allows photonic sensors to estimate insect body orientation ...

A Bayesian-optimized Gaussian process regression framework accurately predicts the splitting tensile strength of concrete containing supplementary cementitious materials using eight ...

A systematic synthetic study shows first-order Noisy Input Gaussian Process Regression sharply outperforms standard GP at high input noise, while ...

A Gaussian process surrogate model trained on 346 fixed-bed adsorption experiments was coupled with genetic and swarm-based optimization to balance ...

A new two-stage reduced order model combines hyper-reduction and Gaussian process regression to cut both training and prediction costs for ...

In one of Tehran's oldest and most crowded municipal districts, researchers have tested whether machine-learning algorithms can untangle the web ...