A data driven BRDF model based on Gaussian process regression

2013 ◽  
Author(s):  
Zhuang Tian ◽  
Dongdong Weng ◽  
Jianying Hao ◽  
Yupeng Zhang ◽  
Dandan Meng
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Data Driven ◽  
Model Based

scholarly journals A Gaussian Process Regression approach within a data-driven POD framework for engineering problems in fluid dynamics

2021 ◽  
Vol 4 (3) ◽  
pp. 1-16
Author(s):  
Giulio Ortali ◽  
◽  
Nicola Demo ◽  
Gianluigi Rozza ◽  
Keyword(s):  
Gaussian Process ◽  
Stokes Problem ◽  
Gaussian Process Regression ◽  
Engineering Problem ◽  
Data Driven ◽  
Regression Approach ◽  
Data Driven Approach ◽  
Naval Engineering ◽  
Proper Orthogonal ◽  
Industrial Problem

<abstract><p>This work describes the implementation of a data-driven approach for the reduction of the complexity of parametrical partial differential equations (PDEs) employing Proper Orthogonal Decomposition (POD) and Gaussian Process Regression (GPR). This approach is applied initially to a literature case, the simulation of the Stokes problem, and in the following to a real-world industrial problem, within a shape optimization pipeline for a naval engineering problem.</p></abstract>

Uniform Design–Based Gaussian Process Regression for Data-Driven Rapid Fragility Assessment of Bridges

2021 ◽  
Vol 147 (4) ◽  
pp. 04021008
Author(s):  
Yutao Pang ◽  
Xiaoyong Zhou ◽  
Wei He ◽  
Jian Zhong ◽  
Ouyang Hui
Keyword(s):  
Gaussian Process ◽  
Uniform Design ◽  
Gaussian Process Regression ◽  
Data Driven

scholarly journals An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression

2018 ◽  
Vol 71 (5) ◽  
pp. 1055-1068 ◽  
Author(s):  
Jianli Zhao ◽  
Xiang Gao ◽  
Xin Wang ◽  
Chunxiu Li ◽  
Min Song ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Gaussian Process ◽  
Predictive Model ◽  
Gaussian Process Regression ◽  
Environment Changes ◽  
Model Based ◽  
Map Updating ◽  
Mean Function ◽  
Radio Map ◽  
Indoor Localisation

Fingerprint-based indoor localisation suffers from influences such as fingerprint pre-collection, environment changes and expending a lot of manpower and time to update the radio map. To solve the problem, we propose an efficient radio map updating algorithm based on K-Means and Gaussian Process Regression (KMGPR). The algorithm builds a Gaussian Process Regression (GPR) predictive model based on a Gaussian mean function and realises the update of the radio map using K-Means. We have conducted experiments to evaluate the performance of the proposed algorithm and results show that GPR using the Gaussian mean function improves localisation accuracy by about 13·76% compared with other functions and KMGPR can reduce the computational complexity by about 7% to 20% with no obvious effects on accuracy.

scholarly journals Minimum Mode Saddle Point Searches Using Gaussian Process Regression with Inverse-Distance Covariance Function

2019 ◽  
Author(s):  
Olli-Pekka Koistinen ◽  
Vilhjálmur Ásgeirsson ◽  
Aki Vehtari ◽  
Hannes Jónsson
Keyword(s):  
Saddle Point ◽  
Gaussian Process ◽  
Process Model ◽  
Energy Surface ◽  
Saddle Points ◽  
Gaussian Process Regression ◽  
Dissociative Adsorption ◽  
Initial Guess ◽  
Initial State ◽  
Model Based

The minimum mode following method can be used to find saddle points on an energy surface by following a direction guided by the lowest curvature mode. Such calculations are often started close to a minimum on the energy surface to find out which transitions can occur from an initial state of the system, but it is also common to start from the vicinity of a first order saddle point making use of an initial guess based on intuition or more approximate calculations. In systems where accurate evaluations of the energy and its gradient are computationally intensive, it is important to exploit the information of the previous evaluations to enhance the performance. Here, we show that the number of evaluations required for convergence to the saddle point can be significantly reduced by making use of an approximate energy surface obtained by a Gaussian process model based on inverse inter-atomic distances, evaluating accurate energy and gradient at the saddle point of the approximate surface and then correcting the model based on the new information. The performance of the method is tested with start points chosen randomly in the vicinity of saddle points for dissociative adsorption of an H2 molecule on the Cu(110) Surface and three gas phase chemical reactions.<br>

scholarly journals Data Driven Prognosis of Fracture Dynamics Using Tensor Train and Gaussian Process Regression

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 222256-222266
Author(s):  
Pham Luu Trung Duong ◽  
Shaista Hussain ◽  
Mark Hyunpong Jhon ◽  
Nagarajan Raghavan
Keyword(s):  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Data Driven ◽  
Fracture Dynamics
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