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Physics-Informed CoKriging: A Gaussian-Process-Regression-Based
  Multifidelity Method for Data-Model Convergence

Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence

24 November 2018
Xiu Yang
D. Barajas-Solano
G. Tartakovsky
A. Tartakovsky
ArXivPDFHTML

Papers citing "Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence"

9 / 9 papers shown
Title
Label Propagation Training Schemes for Physics-Informed Neural Networks
  and Gaussian Processes
Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
Ming Zhong
Dehao Liu
Raymundo Arroyave
U. Braga-Neto
AI4CE
SSL
26
1
0
08 Apr 2024
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network
  Kernel for Gaussian Process Regression
Reconstructing Blood Flow in Data-Poor Regimes: A Vasculature Network Kernel for Gaussian Process Regression
S. Z. Ashtiani
Mohammad Sarabian
K. Laksari
H. Babaee
34
2
0
14 Mar 2024
General multi-fidelity surrogate models: Framework and active learning
  strategies for efficient rare event simulation
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation
Promiti Chakroborty
Somayajulu L. N. Dhulipala
Yifeng Che
Wen Jiang
B. Spencer
J. Hales
Michael D. Shields
AI4CE
29
3
0
07 Dec 2022
Monte Carlo PINNs: deep learning approach for forward and inverse
  problems involving high dimensional fractional partial differential equations
Monte Carlo PINNs: deep learning approach for forward and inverse problems involving high dimensional fractional partial differential equations
Ling Guo
Hao Wu
Xiao-Jun Yu
Tao Zhou
PINN
AI4CE
32
58
0
16 Mar 2022
Failure-averse Active Learning for Physics-constrained Systems
Failure-averse Active Learning for Physics-constrained Systems
Cheolhei Lee
Xing Wang
Jianguo Wu
Xiaowei Yue
AI4CE
19
7
0
27 Oct 2021
Active Learning with Multifidelity Modeling for Efficient Rare Event
  Simulation
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation
Somayajulu L. N. Dhulipala
Michael D. Shields
B. Spencer
C. Bolisetti
A. Slaughter
V. Labouré
P. Chakroborty
32
24
0
25 Jun 2021
Physics-informed CoKriging model of a redox flow battery
Physics-informed CoKriging model of a redox flow battery
Amanda A. Howard
A. Tartakovsky
14
12
0
17 Jun 2021
When Bifidelity Meets CoKriging: An Efficient Physics-Informed
  Multifidelity Method
When Bifidelity Meets CoKriging: An Efficient Physics-Informed Multifidelity Method
Xiu Yang
Xueyu Zhu
Jing Li
21
15
0
07 Dec 2018
Recursive co-kriging model for Design of Computer experiments with
  multiple levels of fidelity with an application to hydrodynamic
Recursive co-kriging model for Design of Computer experiments with multiple levels of fidelity with an application to hydrodynamic
Loic Le Gratiet
AI4CE
88
292
0
02 Oct 2012
1