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machine learning models for inverse design of the electrochemical oxidation process for water purification.  (Gaussian inc)

 
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    Gaussian inc machine learning models for inverse design of the electrochemical oxidation process for water purification.
    Machine Learning Models For Inverse Design Of The Electrochemical Oxidation Process For Water Purification., supplied by Gaussian 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+model/machine+learning+models+for+inverse+design+of+the+electrochemical+oxidation+process+for+water+purification+/pm39772452-315-34-2
    Average 90 stars, based on 1 article reviews
    machine learning models for inverse design of the electrochemical oxidation process for water purification. - by Bioz Stars, 2026-09
    90/100 stars

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    Related Articles

    Purification:

    Article Title: Interpretable Machine Learning Models Delivering a New Perspective for the Reaction Mechanism between Organic Pollutants and Oxidative Radicals.
    Article Snippet: Machine learning (ML) is expected to bring new insights into the impact of organic structures on the reaction mechanisms in reactive oxygen species oxidation.. However, understanding the underlying chemical mechanisms still faces challenges due to the limited interpretability of the ML models.. In this study, interpretable ML models were established to predict the second-order rate constants between hydroxyl radicals (•OH) and organics (k•OH).



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    (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