The CINT method combines classical Galerkin methods with a constrained backpropogation training approach to obtain an artificial neural network representation of the PDE solution that approximately satisfies the boundary conditions at every integration haberman pde solutions 2.5.6 a pdf. The advantage of CINT over existing methods is that it is readily applicable to solving PDEs on irregular domains, and requires no special modification for domains with complex geometries. Furthermore, the CINT method provides a semi-analytical solution that is infinitely differentiable.
In this paper the CINT method is demonstrated on two hyperbolic and one parabolic initial boundary value problems with a known analytical solutions that can be used for performance comparison. The numerical results show that, when compared to the most efficient finite element methods, the CINT method achieves significant improvements both in terms of computational time and accuracy. Salt Lake City, UT, USA. Brigham Young University, Provo, UT, USA, in 2007, the M.
Northwestern University, Evanston, IL, USA, in 2008, and the Master of Engineering Management degree and the Ph. Duke University, Durham, NC, USA, in 2010 and 2013, respectively. Her principal research interests include robust adaptive control of aircraft, learning and approximate dynamic programming, and optimal control of mobile sensor networks. Embry-Riddle Aeronautical University and the M. She is a senior member of the IEEE, and a member of ASME, SPIE, and AIAA. Enter a search term to search UC pages or the directory.
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