ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1701.02440
  4. Cited By
Machine Learning of Linear Differential Equations using Gaussian
  Processes

Machine Learning of Linear Differential Equations using Gaussian Processes

10 January 2017
M. Raissi
George Karniadakis
ArXivPDFHTML

Papers citing "Machine Learning of Linear Differential Equations using Gaussian Processes"

14 / 14 papers shown
Title
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis
Yasamin Jalalian
Juan Felipe Osorio Ramirez
Alexander W. Hsu
Bamdad Hosseini
H. Owhadi
128
0
0
02 Mar 2025
Al-Khwarizmi: Discovering Physical Laws with Foundation Models
Al-Khwarizmi: Discovering Physical Laws with Foundation Models
Christopher E. Mower
Haitham Bou-Ammar
AI4CE
125
2
0
03 Feb 2025
Regression Trees Know Calculus
Regression Trees Know Calculus
Nathan Wycoff
47
0
0
22 May 2024
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory
I. Kavrakov
Gledson Rodrigo Tondo
Guido Morgenthal
AI4CE
79
1
0
21 May 2024
Numerical Gaussian Processes for Time-dependent and Non-linear Partial
  Differential Equations
Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations
M. Raissi
P. Perdikaris
George Karniadakis
53
267
0
29 Mar 2017
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse
  Problems
Probabilistic Numerical Methods for PDE-constrained Bayesian Inverse Problems
Jon Cockayne
Chris J. Oates
T. Sullivan
Mark Girolami
AI4CE
47
80
0
15 Jan 2017
Inferring solutions of differential equations using noisy multi-fidelity
  data
Inferring solutions of differential equations using noisy multi-fidelity data
M. Raissi
P. Perdikaris
George Karniadakis
AI4CE
41
288
0
16 Jul 2016
Probabilistic Numerical Methods for Partial Differential Equations and
  Bayesian Inverse Problems
Probabilistic Numerical Methods for Partial Differential Equations and Bayesian Inverse Problems
Jon Cockayne
Chris J. Oates
T. Sullivan
Mark Girolami
59
45
0
25 May 2016
Bayesian Numerical Homogenization
Bayesian Numerical Homogenization
H. Owhadi
62
234
0
25 Jun 2014
Manifold Gaussian Processes for Regression
Manifold Gaussian Processes for Regression
Roberto Calandra
Jan Peters
C. Rasmussen
M. Deisenroth
144
272
0
24 Feb 2014
Gaussian Processes for Big Data
Gaussian Processes for Big Data
J. Hensman
Nicolò Fusi
Neil D. Lawrence
GP
83
1,226
0
26 Sep 2013
Structure Discovery in Nonparametric Regression through Compositional
  Kernel Search
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
David Duvenaud
J. Lloyd
Roger C. Grosse
J. Tenenbaum
Zoubin Ghahramani
62
509
0
20 Feb 2013
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
111
294
0
02 Oct 2012
Linear Latent Force Models using Gaussian Processes
Linear Latent Force Models using Gaussian Processes
Mauricio A. Alvarez
D. Luengo
Neil D. Lawrence
49
124
0
13 Jul 2011
1