Search Results for "finite difference"

This software has moved to http://www.poissonboltzmann.org/.

GMES is a free finite-difference time-domain (FDTD) simulation Python package developed at GIST to model photonic devices. Its features include simulation in 1D, 2D, and 3D Cartesian coordinates, distributed memory parallelism on any system supporting the MPI standard, portable to any Unix-like system, variuos dispersive ε(ω) models, CPML absorbing boundaries and/or Bloch-periodic boundary conditions, and arbitrary material and source distributions.

KEMP is a fast FDTD solver on a GPU-based cluster. The FDTD (Finite-Difference Time-Domain) method is a popular numerical method for electromagnetic field simulations. KEMP enables hardware accelerations suitable for multi-GPU, multi-core CPU and GPU cluster. KEMP also provide easy configuration by using Python scripting language.

WOLFSIM is a Finite-Difference Time-Domain electromagnetic simulator, designed to be easy to use but still very powerful, developed and maintained by researchers at North Carolina State University. It's features include: -1D, 2D, and 3D structures that are periodic in 1 or 2 dimensions -Materials that are anisotropic in permittivity and conductivity -Obliquely incident sources -Built-in vectorial (i.e. full polarization) near-to-far-field transformation See these publications for full details on the algorithm: http://www.ece.ncsu.edu/oleg/files-wiki/6/62/SPIE_12_Miskiewicz_wolfsim3D.pdf http://www.ece.ncsu.edu/oleg/files-wiki/1/13/OptExpress07_OH.pdf

The parallelized FDTD Schrodinger Solver implements a parallel algorithm for solving the time-independent 3d Schrodinger equation using the finite difference time domain (FDTD) method. See the Hosted Apps > MediaWiki menu item for more information.

SPSens is a complete software package written in C that estimates parameter sensitivities for stochastic models of chemical and biochemical reaction networks using Monte Carlo (MC) stochastic simulations. It is possible to estimate sensitivities with respect to system parameters using the following algorithms: finite difference methods (crude monte carlo, common reaction path, coupled finite differences); likelihood ratio methods; and regularized pathwise derivatives. Additionally the package includes basic stochastic simulation algorithms. The package includes several example networks which can be easily modified for other networks. Serial and parallel MPI implementations can be built and called from the command line. ...

GGGG's a Great Grid Generator (4G) is a boundary fitted, curvilinear, grid generation tool for 2-d finite difference modeling. It is written in Python with the Numeric extensions. Most classes have been written, but it does nothing useful, yet...