Machine learning of linear differential equations using Gaussian processes-Reference-Cited by-同舟云学术

Machine learning of linear differential equations using Gaussian processes

Published:2017-11 Issue: Volume:348 Page:683-693
ISSN:0021-9991
Container-title:Journal of Computational Physics
language:en
Short-container-title:Journal of Computational Physics

Author:

Raissi Maziar,Perdikaris Paris,Karniadakis George Em

Funder

Defense Advanced Research Projects Agency

Air Force Office of Scientific Research

Publisher

Elsevier BV

Subject

Computer Science Applications,Physics and Astronomy (miscellaneous),Applied Mathematics,Computational Mathematics,Modelling and Simulation,Numerical Analysis

Reference53 articles.

1. Sparse convolved Gaussian processes for multi-output regression;Alvarez,2009

2. Latent force models;Alvarez,2009

3. Linear latent force models using Gaussian processes;Álvarez;IEEE Trans. Pattern Anal. Mach. Intell.,2013

4. Theory of reproducing kernels;Aronszajn;Trans. Am. Math. Soc.,1950

5. Reproducing Kernel Hilbert Spaces in Probability and Statistics;Berlinet,2011

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