GPR
47 papers with code • 0 benchmarks • 1 datasets
Gaussian Process Regression
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Latest papers with no code
Kernel Multigrid: Accelerate Back-fitting via Sparse Gaussian Process Regression
By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fitting is no faster than $(1-\mathcal{O}(\frac{1}{n}))^t$, where $n$ and $t$ denote the data size and the iteration number, respectively.
Bayesian Optimization that Limits Search Region to Lower Dimensions Utilizing Local GPR
We propose a BO that limits the search region to lower dimensions and utilizes local Gaussian process regression (LGPR) to scale the BO to higher dimensions.
A novel data generation scheme for surrogate modelling with deep operator networks
In general, towards operator surrogate modeling, the training data is generated by solving the PDEs using techniques such as Finite Element Method (FEM).
Gaussian-process-regression-based method for the localization of exceptional points in complex resonance spectra
Their exact localization in the parameter space is challenging, in particular in systems, where the computation of the quantum spectra and resonances is numerically very expensive.
Sparse discovery of differential equations based on multi-fidelity Gaussian process
Sparse identification of differential equations aims to compute the analytic expressions from the observed data explicitly.
Real-Time Asphalt Pavement Layer Thickness Prediction Using Ground-Penetrating Radar Based on a Modified Extended Common Mid-Point (XCMP) Approach
This study investigates the affecting factors and develops a modified XCMP method to allow automatic thickness prediction of in-service asphalt pavement with non-uniform dielectric properties through depth.
Bayesian inversion of GPR waveforms for uncertainty-aware sub-surface material characterization
In addition, the estimation of the properties of the overlaying layer is crucial for applications like wildfire assessment.
KF-PLS: Optimizing Kernel Partial Least-Squares (K-PLS) with Kernel Flows
Only a few studies have been conducted on optimizing the kernel parameters for K-PLS.
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.
