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gaussian process regression models (gpr)  (MathWorks Inc)


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    MathWorks Inc gaussian process regression models (gpr)
    Gaussian Process Regression Models (Gpr), supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/gaussian+process+regression+model/10__1016_slash_j__ces__2024__120595-106-17-33
    Average 90 stars, based on 1 article reviews
    gaussian process regression models (gpr) - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Generalizing post-stroke prognoses from research data to clinical data
    Article Snippet: Prognostic models were trained with a Gaussian Process regression model with default hyper-parameters, as specified for Matlab 2017a.

    Article Title: Mental chronometry in big noisy data
    Article Snippet: In order to increase the temporal resolution of this measure, data were fitted by a Gaussian process regression model as implemented in the Statistics and Machine Learning Toolbox of MATLAB [ ] that was applied with a temporal resolution of 1 MHz.

    Article Title: Modeling and optimization of galvanometric point-scanning temporal dynamics
    Article Snippet: A Gaussian process regression (GPR) model of settling time values was created (MATLAB, MathWorks) to narrow down the range of optimal basic parameter values over the complex nonlinear parameter space.

    Construct:

    Article Title: Advancement of sustainable trypan blue degradation through rice husk ash based-heterogenous Fenton catalyst and study with gaussian process regression
    Article Snippet: Dyes and their derivatives have been found to be harmful to aquatic organisms, and discharging dye effluent without proper treatment into the environment can lead to affect the usability of water for distinct purposes, including drinking water, agriculture, and commercial processes.. To overcome this environmental crisis a costeffective rice husk ash (RHA) derived catalyst was synthesized as Fenton catalyst and a new, environmentally, and friendly treatment was used to treat trypan blue (TB) from synthetic wastewater.. SEM, EDX, XRD, and FTIR were used to describe the prepared catalyst’s comprehensive characterization.



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    (a) Normalized Root Mean Square Error (nRMSE) values between the <t>Gaussian</t> Predictor Response <t>(GPR)</t> predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .
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    (a) Normalized Root Mean Square Error (nRMSE) values between the <t>Gaussian</t> Predictor Response <t>(GPR)</t> predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .
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    (a) Normalized Root Mean Square Error (nRMSE) values between the <t>Gaussian</t> Predictor Response <t>(GPR)</t> predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .
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    (a) Normalized Root Mean Square Error (nRMSE) values between the <t>Gaussian</t> Predictor Response <t>(GPR)</t> predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .
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    Image Search Results


    (a) Normalized Root Mean Square Error (nRMSE) values between the Gaussian Predictor Response (GPR) predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .

    Journal: Earth's Future

    Article Title: The Nonlinear and Distinct Responses of Ocean Heat Content and Anthropogenic Carbon to Ice Sheet Freshwater Discharge in a Warming Climate

    doi: 10.1029/2024EF004475

    Figure Lengend Snippet: (a) Normalized Root Mean Square Error (nRMSE) values between the Gaussian Predictor Response (GPR) predicted OHC ANTH and actual OHC ANTH generated by withholding one predictor at a time for the Control (gray), AIS (blue), GrIS (pink), and AGrIS (black) simulations. (b) Same as for panel (a) but for C ANTH .

    Article Snippet: Here, we identify the different driving factors in the FW linear and nonlinear OHC ANTH and C ANTH responses using a predictive, Gaussian Process Regression (GPR) model in MATLAB's Regression Learner toolbox.

    Techniques: Generated, Control

    Regression analysis of baseline Multilinear Regression and Machine Learning methods—Gaussian Process Regression and Bayesian Regularised Artificial Neural Network. Output along the y -axis shows the normalised output of the surrogate at each of the 32 designs and Target along the x -axis shows normalised simulations results for efficiency at these design points.

    Journal: Scientific Reports

    Article Title: Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades

    doi: 10.1038/s41598-023-33708-9

    Figure Lengend Snippet: Regression analysis of baseline Multilinear Regression and Machine Learning methods—Gaussian Process Regression and Bayesian Regularised Artificial Neural Network. Output along the y -axis shows the normalised output of the surrogate at each of the 32 designs and Target along the x -axis shows normalised simulations results for efficiency at these design points.

    Article Snippet: A Gaussian Process Regression model was implemented in MATLAB using a five-fold cross-validation method whereby the data is partitioned to exclude a fifth of available set for training a validation.

    Techniques:

    Comparison of previously best performing pump design from the original 32 base designs and the new optimised blade design at selected operating point of Q = 2 L/min, H = 70 mmHg—the result of the optimisation routine utilising constraint iteration 3 and the Gaussian process regression surrogate model.

    Journal: Scientific Reports

    Article Title: Machine learning based on computational fluid dynamics enables geometric design optimisation of the NeoVAD blades

    doi: 10.1038/s41598-023-33708-9

    Figure Lengend Snippet: Comparison of previously best performing pump design from the original 32 base designs and the new optimised blade design at selected operating point of Q = 2 L/min, H = 70 mmHg—the result of the optimisation routine utilising constraint iteration 3 and the Gaussian process regression surrogate model.

    Article Snippet: A Gaussian Process Regression model was implemented in MATLAB using a five-fold cross-validation method whereby the data is partitioned to exclude a fifth of available set for training a validation.

    Techniques: