A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification

13 May 2022

Papers citing "A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification"

6 / 6 papers shown

Title
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks Yibo Yang P. Perdikaris AI4CE PINN 75 356 0 09 Nov 2018
The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems E. Weinan Ting Yu 83 1,373 0 30 Sep 2017
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network C. Ledig Lucas Theis Ferenc Huszár Jose Caballero Andrew Cunningham ... Andrew P. Aitken Alykhan Tejani J. Totz Zehan Wang Wenzhe Shi GAN 215 10,646 0 15 Sep 2016
Deep Kernel Learning A. Wilson Zhiting Hu Ruslan Salakhutdinov Eric Xing BDL 151 882 0 06 Nov 2015
Deep Gaussian Processes Andreas C. Damianou Neil D. Lawrence GP BDL 51 1,178 0 02 Nov 2012
Linear Latent Force Models using Gaussian Processes Mauricio A. Alvarez D. Luengo Neil D. Lawrence 39 124 0 13 Jul 2011