AbstractGeneric operator equations allowing multidimensional operators are solved by a decomposition method allowing solution of nonlinear and/or stochastic partial differential equations by accurate and convenient approximation. Green's functions for complicated ordinary or partial differential linear equations are similarly determinable
AbstractWe consider the solution of partial differential equations for initial/boundary conditions u...
The classical partial differential equations of applied mathematics: diffusion, Laplace/Poisson, and...
AbstractThe decomposition method (“Stochastic systems,” Academic Press, New York 1983; “Nonlinear St...
AbstractGeneric operator equations allowing multidimensional operators are solved by a decomposition...
AbstractApplication of the decomposition method and of the asymptotic decomposition method are consi...
AbstractIn Adomian's solution of linear or nonlinear, deterministic or stochastic, differential equa...
AbstractA number of important equations of physics are shown to fit into the structure of a nonlinea...
AbstractComputationally efficient physically accurate solutions have been found for broad classes of...
AbstractWe present a further development of the decomposition method [1,2], which leads to a single ...
AbstractThe decomposition method is applied to solution of partial differential equations in two and...
AbstractThe author's decomposition method for the solution of operator equations which may be nonlin...
This paper focuses on the efficient solution of models defined in high dimensional spaces. Those mod...
AbstractThe operator-theoretic (or inverse) method for stochastic differential equations is generali...
AbstractRecent generalizations are discussed and results are presented for the theory and applicatio...
This paper revisits a powerful discretization technique, the Proper Generalized Decomposition—PGD, i...
AbstractWe consider the solution of partial differential equations for initial/boundary conditions u...
The classical partial differential equations of applied mathematics: diffusion, Laplace/Poisson, and...
AbstractThe decomposition method (“Stochastic systems,” Academic Press, New York 1983; “Nonlinear St...
AbstractGeneric operator equations allowing multidimensional operators are solved by a decomposition...
AbstractApplication of the decomposition method and of the asymptotic decomposition method are consi...
AbstractIn Adomian's solution of linear or nonlinear, deterministic or stochastic, differential equa...
AbstractA number of important equations of physics are shown to fit into the structure of a nonlinea...
AbstractComputationally efficient physically accurate solutions have been found for broad classes of...
AbstractWe present a further development of the decomposition method [1,2], which leads to a single ...
AbstractThe decomposition method is applied to solution of partial differential equations in two and...
AbstractThe author's decomposition method for the solution of operator equations which may be nonlin...
This paper focuses on the efficient solution of models defined in high dimensional spaces. Those mod...
AbstractThe operator-theoretic (or inverse) method for stochastic differential equations is generali...
AbstractRecent generalizations are discussed and results are presented for the theory and applicatio...
This paper revisits a powerful discretization technique, the Proper Generalized Decomposition—PGD, i...
AbstractWe consider the solution of partial differential equations for initial/boundary conditions u...
The classical partial differential equations of applied mathematics: diffusion, Laplace/Poisson, and...
AbstractThe decomposition method (“Stochastic systems,” Academic Press, New York 1983; “Nonlinear St...
DOI
Add to ORKG