Partial Differential Equations with Random Input Data



Functional Differential and Difference Equations with Applications 2013
Diblik, J.; Braverman, E.; Gyori, I.; Rogovchenko, Yu.; Ruzickova, M.; Zafer, Ağacık (2014-01-01)
Differential quadrature solution of nonlinear Klein-Gordon and sine-Gordon equations
Pekmen, B.; Tezer, Münevver (2012-08-01)
Differential quadrature method (DQM) is proposed to solve the one-dimensional quadratic and cubic Klein-Gordon equations, and two-dimensional sine-Gordon equation. We apply DQM in space direction and also blockwise in time direction. Initial and derivative boundary conditions are also approximated by DQM. DQM provides one to obtain numerical results with very good accuracy using considerably small number of grid points. Numerical solutions are obtained by using Gauss-Chebyshev-Lobatto (GCL) grid points in s...
Numerical solutions of the lorenz and van der pol equations by evolution operator method
Aladl, Usef Emhamed; Ergenç, Tanıl; Department of Mathematics (1992)
Numerical solutions for the Navier-Stokes equations in primitive variables using finite-difference method
Omari, Rea'd; Tezer, Münevver; Department of Mathematics (1990)
Differential - Operator solutions for complex partial differential equations
Celebi, O; Sengul, S (1998-07-10)
The solutions of complex partial differential equations of order four are obtained by using polynomial differential operators. A correspondence principle is also derived for the solutions of two different differential equations, imposing conditions on the coefficients.
Citation Formats
H. Yücel, “Partial Differential Equations with Random Input Data,” presented at the COST Action Mat-Dyn-Net Meeting in Zagreb, Zagreb, Hırvatistan, 2020, Accessed: 00, 2021. [Online]. Available: