Life-Cycle Optimization of the CO2 Huff-N-Puff Process in an Unconventional Oil Reservoir using Least-Squares Support-Vector and Gaussian Process Regression Proxies

2020 ◽  
Author(s):  
Azad Almasov ◽  
Mustafa Onur
Keyword(s):  
Life Cycle ◽  
Gaussian Process ◽  
Least Squares ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Oil Reservoir ◽  
Unconventional Oil ◽  
Life Cycle Optimization

scholarly journals Comparison of partial least squares regression, least squares support vector machines, and Gaussian process regression for a near infrared calibration

2017 ◽  
Vol 25 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Chenhao Cui ◽  
Tom Fearn
Keyword(s):  
Support Vector Machines ◽  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares Regression ◽  
Linear Models ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Least Squares Regression ◽  
Vector Machines ◽  
Non Linear

This paper investigates the use of least squares support vector machines and Gaussian process regression for multivariate spectroscopic calibration. The performances of these two non-linear regression models are assessed and compared to the traditional linear regression model, partial least squares regression on an agricultural example. The non linear models, least squares support vector machines, and Gaussian process regression, showed enhanced generalization ability, especially in maintaining homogeneous prediction accuracy over the range. The two non-linear models generally have similar prediction performance, but showed different features in some situations, especially when the size of the training set varies. This is due to fundamental differences in fitting criteria between these models.

scholarly journals Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

2021 ◽  
Vol 11 (9) ◽  
pp. 4055
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Predictive Accuracy ◽  
Gaussian Process Regression ◽  
Machining Process ◽  
Support Vector ◽  
Process Data ◽  
Cutting Force Prediction ◽  
Artificial Neural Network Ann ◽  
Aisi 4340

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

scholarly journals Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression

2014 ◽  
Vol 6 (1) ◽  
Author(s):  
Martin Bogdan ◽  
Dominik Brugger ◽  
Wolfgang Rosenstiel ◽  
Bernd Speiser
Keyword(s):  
Gaussian Process ◽  
Diffusion Coefficients ◽  
Gaussian Process Regression ◽  
Support Vector

Semi-supervised least squares support vector machine algorithm: application to offshore oil reservoir

2016 ◽  
Vol 13 (2) ◽  
pp. 406-415 ◽  
Author(s):  
Wei-Ping Luo ◽  
Hong-Qi Li ◽  
Ning Shi
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Support Vector ◽  
Oil Reservoir ◽  
Support Vector Machine Algorithm ◽  
Offshore Oil

scholarly journals Prediction of Geotechnical Properties of Soil using Artificial Intelligence Framework

2021 ◽  
Vol 10 (4) ◽  
pp. 218-227
Author(s):  
Jitendra Khatti ◽  
◽  
Dr. Kamaldeep Singh Grover ◽  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Clay Content ◽  
Geotechnical Properties ◽  
Support Vector ◽  
Analysis Tool ◽  
Artificial Neural ◽  
Properties Of Soil

The present research work is carried out to predict the geotechnical properties (consistency limits, OMC, and MDD) of soil using AI technologies, namely regression analysis (RA), support vector machine (SVM), Gaussian process regression (GPR), artificial neural networks (ANNs), and relevance vector machine (RVM). The models of machine learning (SVM, GPR), hybrid learning (RVM), and deep learning (ANNs) are constructed in MATLAB R2020a with different configurations. The models of RA are built using the Data Analysis Tool of Microsoft Excel 2019. The input parameters of AI models are gravel, sand, silt, and clay content. The correlation coefficient is calculated for pair of soil datasets. The correlation shows that sand, silt, and clay content play a vital role in predicting soil's liquid limit and plasticity index. The performance of constructed AI models is compared to determine the optimum performance models. The limited datasets of soil are used in this study. Therefore, artificial neural networks and relevance vector machines could not perform well. Based on the performance of AI models, the Gaussian process regression outperformed the RA, SVM, ANNs, and RVM AI technologies. Hence, the GPR AI approach can predict the geotechnical properties of soil by gravel, sand, silt, and clay content. The Monte-Carlo global sensitivity analysis is also performed, and it is observed that the prediction of geotechnical properties of soil is affected by sand and clay content

scholarly journals Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos

2015 ◽  
pp. 55 ◽  
Author(s):  
Ll. Pérez-Planells ◽  
J. Delegido ◽  
J. P. Rivera-Caicedo ◽  
J. Verrelst
Keyword(s):  
Linear Regression ◽  
Gaussian Process ◽  
Least Squares ◽  
Partial Least Squares ◽  
Ridge Regression ◽  
Partial Least Squares Regression ◽  
Gaussian Process Regression ◽  
Least Squares Regression ◽  
Kernel Ridge Regression

<p class="Bodytext">Los métodos de regresión no paramétricos son una gran herramienta estadística para obtener parámetros biofísicos a partir de medidas realizadas mediante teledetección. Pero los resultados obtenidos se pueden ver afectados por los datos utilizados en la fase de entrenamiento del modelo. Para asegurarse de que los modelos son robustos, se hace uso de varias técnicas de validación cruzada. Estas técnicas permiten evaluar el modelo con subconjuntos de la base de datos de campo. Aquí, se evalúan dos tipos de validación cruzada en el desarrollo de modelos de regresión no paramétricos: hold-out y k-fold. Los métodos de regresión lineal seleccionados fueron: Linear Regression (LR) y Partial Least Squares Regression (PLSR). Y los métodos no lineales: Kernel Ridge Regression (KRR) y Gaussian Process Regression (GPR). Los resultados de la validación cruzada mostraron que LR ofrece los resultados más inestables, mientras KRR y GPR llevan a resultados más robustos. Este trabajo recomienda utilizar algoritmos de regresión no lineales (como KRR o GPR) combinando con la validación cruzada k-fold con un valor de k igual a 10 para hacer la estimación de una manera robusta.</p>

scholarly journals Prediction of specific cutting forces and maximum tool temperatures in orthogonal machining by Support Vector and Gaussian Process Regression Methods

2020 ◽  
Vol 48 ◽  
pp. 1000-1008
Author(s):  
Maryam Hashemitaheri ◽  
Sai Manish Reddy Mekarthy ◽  
Harish Cherukuri
Keyword(s):  
Gaussian Process ◽  
Cutting Forces ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Regression Methods ◽  
Orthogonal Machining
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