scholarly journals Machine Learning Based on Kernel Function Controlled Gaussian Process Regression Method for In-depth Extrapolative Analysis of Covid-19 Daily Cases Drift Rates

2021 ◽  
Vol 7 (2) ◽  
pp. 14-23
Author(s):  
Joseph Isabona ◽  
◽  
Divine O. Ojuh
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Kernel Function ◽  
Gaussian Process Regression ◽  
Regression Method

A comparative study of Gaussian process regression with other three machine learning approaches in the performance prediction of centrifugal pump

2021 ◽  
pp. 095440622110505
Author(s):  
Xutao Zhao ◽  
Desheng Zhang ◽  
Renhui Zhang ◽  
Bin Xu
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Centrifugal Pump ◽  
Kernel Function ◽  
Performance Prediction ◽  
Optimization Design ◽  
Gaussian Process Regression ◽  
Kernel Functions ◽  
Performance Indices ◽  
Pump Performance

Accurate prediction of performance indices using impeller parameters is of great importance for the initial and optimal design of centrifugal pump. In this study, a kernel-based non-parametric machine learning method named with Gaussian process regression (GPR) was proposed, with the purpose of predicting the performance of centrifugal pump with less effort based on available impeller parameters. Nine impeller parameters were defined as model inputs, and the pump performance indices, that is, the head and efficiency, were determined as model outputs. The applicability of three widely used nonlinear kernel functions of GPR including squared exponential (SE), rational quadratic (RQ) and Matern5/2 was investigated, and it was found by comparing with the experimental data that the SE kernel function is more suitable to capture the relationship between impeller parameters and performance indices because of the highest R square and the lowest values of max absolute relative error (MARE), mean absolute proportional error (MAPE), and root mean square error (RMSE). In addition, the results predicted by GPR with SE kernel function were compared with the results given by other three machine learning models. The comparison shows that the GPR with SE kernel function is more accurate and robust than other models in centrifugal pump performance prediction, and its prediction errors and uncertainties are both acceptable in terms of engineering applications. The GPR method is less costly in the performance prediction of centrifugal pump with sufficient accuracy, which can be further used to effectively assist the design and manufacture of centrifugal pump and to speed up the optimization design process of impeller coupled with stochastic optimization methods.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

scholarly journals Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation

2021 ◽  
Vol 88 (2) ◽  
pp. 27-37
Author(s):  
Sachin Dev Suresh ◽  
Ali Qasim ◽  
Bhajan Lal ◽  
Syed Muhammad Imran ◽  
Khor Siak Foo
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gas Hydrate ◽  
Learning Algorithm ◽  
Hydrate Formation ◽  
Gaussian Process Regression ◽  
Normal Operation ◽  
Coefficient Of Determination ◽  
Gas Hydrate Formation ◽  
Testing Data

The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country’s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25% and 96.71%, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively.

TRAJECTORY PLANNING OF 6-DOF MANIPULATOR BASED ON GAUSSIAN PROCESS REGRESSION METHOD

2020 ◽  
Vol 35 (3) ◽  
Author(s):  
Jinzhi Zhao ◽  
Shizhao Wang ◽  
Aibing Jiang ◽  
Jin Xiao ◽  
Bin Wang
Keyword(s):  
Gaussian Process ◽  
Trajectory Planning ◽  
Gaussian Process Regression ◽  
Regression Method

Soft sensor for the moisture content of crude oil based on multi-kernel Gaussian process regression optimized by an adaptive variable population fruit fly optimization algorithm

2019 ◽  
Vol 42 (4) ◽  
pp. 770-785 ◽  
Author(s):  
Kun Li ◽  
Wensu Xu ◽  
Ying Han ◽  
Fawei Ge ◽  
Yi’an Wang
Keyword(s):  
Moisture Content ◽  
Crude Oil ◽  
Gaussian Process ◽  
Kernel Function ◽  
Optimization Algorithm ◽  
Gaussian Process Regression ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Variable Population

In the practical oilfield production, it has great significance to realize timely and accurate measurement of the moisture content of crude oil. However, there are some drawbacks in the traditional measurement methods, such as: non-real time, high cost, labor-consume, vulnerability to environmental impacts, and so on. In order to solve these problems, a soft sensor model based on multi-kernel Gaussian process regression optimized by an adaptive variable population fruit fly optimization algorithm (APFOA-MKGPR) is presented in this paper. A multiple kernels-based Gaussian process regression method is utilized to deal with the practical production process characterised by multiple operating phases, noises, strong nonlinearity and dynamic. In the multi-kernel function, many parameters (five hyper-parameters in the multi-kernel function and three weights of each kernel function) need to be accurately given, which is difficult to be effectively optimized by the maximum likelihood estimation. So, a swarm intelligence-based adaptive variable population fruit fly optimization algorithm (APFOA) is proposed to train the best model parameters. A novel adaptive variable population mechanism is developed to adaptively adjust the population size and the random flight distance during the iterations, which can realize a combination of the global searching and the local searching for the optimal solutions. The proposed method is verified by four benchmark functions and the actual production data of one oil well, and experimental results show the effectiveness for accurate prediction of the moisture content of crude oil.

scholarly journals Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data

2022 ◽  
Author(s):  
Sergei Manzhos ◽  
Eita Sasaki ◽  
Manabu Ihara
Keyword(s):  
Machine Learning ◽  
Kinetic Energy ◽  
Potential Energy ◽  
Gaussian Process ◽  
Potential Energy Surfaces ◽  
Gaussian Process Regression ◽  
Multivariate Functions ◽  
Low Dimensional ◽  
Energy Surfaces ◽  
Kernel Design

Abstract We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with HDMR (High-dimensional model representation), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster and simpler to use. We tested the approach on cases where highly accurate machine learning is required from sparse data by fitting potential energy surfaces and kinetic energy densities.

scholarly journals Green LAI Mapping and Cloud Gap-Filling Using Gaussian Process Regression in Google Earth Engine

2021 ◽  
Vol 13 (3) ◽  
pp. 403
Author(s):  
Luca Pipia ◽  
Eatidal Amin ◽  
Santiago Belda ◽  
Matías Salinero-Delgado ◽  
Jochem Verrelst
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Google Earth ◽  
Machine Learning Techniques ◽  
Gap Filling ◽  
Area Index ◽  
Uncertainty Estimates ◽  
Google Earth Engine

For the last decade, Gaussian process regression (GPR) proved to be a competitive machine learning regression algorithm for Earth observation applications, with attractive unique properties such as band relevance ranking and uncertainty estimates. More recently, GPR also proved to be a proficient time series processor to fill up gaps in optical imagery, typically due to cloud cover. This makes GPR perfectly suited for large-scale spatiotemporal processing of satellite imageries into cloud-free products of biophysical variables. With the advent of the Google Earth Engine (GEE) cloud platform, new opportunities emerged to process local-to-planetary scale satellite data using advanced machine learning techniques and convert them into gap-filled vegetation properties products. However, GPR is not yet part of the GEE ecosystem. To circumvent this limitation, this work proposes a general adaptation of GPR formulation to parallel processing framework and its integration into GEE. To demonstrate the functioning and utility of the developed workflow, a GPR model predicting green leaf area index (LAIG) from Sentinel-2 imagery was imported. Although by running this GPR model into GEE any corner of the world can be mapped into LAIG at a resolution of 20 m, here we show some demonstration cases over western Europe with zoom-ins over Spain. Thanks to the computational power of GEE, the mapping takes place on-the-fly. Additionally, a GPR-based gap filling strategy based on pre-optimized kernel hyperparameters is also put forward for the generation of multi-orbit cloud-free LAIG maps with an unprecedented level of detail, and the extraction of regularly-sampled LAIG time series at a pixel level. The ability to plugin a locally-trained GPR model into the GEE framework and its instant processing opens up a new paradigm of remote sensing image processing.

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