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Analytically Embedding Differential Equation Constraints into Least
  Squares Support Vector Machines using the Theory of Functional Connections
v1v2v3 (latest)

Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines using the Theory of Functional Connections

13 December 2018
Carl Leake
Hunter Johnston
Lidia Smith
D. Mortari
ArXiv (abs)PDFHTML

Papers citing "Analytically Embedding Differential Equation Constraints into Least Squares Support Vector Machines using the Theory of Functional Connections"

3 / 3 papers shown
Title
A Physics-Informed Machine Learning Approach for Solving Distributed
  Order Fractional Differential Equations
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations
Alireza Afzal Aghaei
41
0
0
05 Sep 2024
Extreme Theory of Functional Connections: A Physics-Informed Neural
  Network Method for Solving Parametric Differential Equations
Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations
E. Schiassi
Carl Leake
Mario De Florio
Hunter Johnston
R. Furfaro
D. Mortari
PINN
16
11
0
15 May 2020
Deep Theory of Functional Connections: A New Method for Estimating the
  Solutions of PDEs
Deep Theory of Functional Connections: A New Method for Estimating the Solutions of PDEs
Carl Leake
61
69
0
20 Dec 2018
1