GPR
47 papers with code • 0 benchmarks • 1 datasets
Gaussian Process Regression
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Latest papers with no code
On the variants of SVM methods applied to GPR data to classify tack coat characteristics in French pavements: two experimental case studies
Among the commonly used non-destructive techniques, the Ground Penetrating Radar (GPR) is one of the most widely adopted today for assessing pavement conditions in France.
Optimal Data Generation in Multi-Dimensional Parameter Spaces, using Bayesian Optimization
Our work contributes to the resource-efficient collection of data in high-dimensional complex parameter spaces, to achieve high precision machine learning predictions.
Novel models for fatigue life prediction under wideband random loads based on machine learning
Machine learning as a data-driven solution has been widely applied in the field of fatigue lifetime prediction.
A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-dimensional American Options
In this work, we present a novel machine learning approach for pricing high-dimensional American options based on the modified Gaussian process regression (GPR).
Guaranteed Coverage Prediction Intervals with Gaussian Process Regression
Gaussian Process Regression (GPR) is a popular regression method, which unlike most Machine Learning techniques, provides estimates of uncertainty for its predictions.
Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction
We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon.
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation
In this study, a physics-informed Gaussian process regression (GPR) model is developed for battery SOH estimation, with the performance being systematically compared with that of different types of features and machine learning (ML) methods.
Learning-based Control for PMSM Using Distributed Gaussian Processes with Optimal Aggregation Strategy
The growing demand for accurate control in varying and unknown environments has sparked a corresponding increase in the requirements for power supply components, including permanent magnet synchronous motors (PMSMs).
Spectral-Bias and Kernel-Task Alignment in Physically Informed Neural Networks
Physically informed neural networks (PINNs) are a promising emerging method for solving differential equations.
Bayesian tomography using polynomial chaos expansion and deep generative networks
By sampling a low-dimensional prior probability distribution defined in the low-dimensional latent space of such a model, it becomes possible to efficiently sample the physical domain at the price of a generator that is typically highly non-linear.
