3 projects for "binary differential evolution" with 2 filters applied:

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  • 1

    Fosite - advection problem solver

    numerical simulation code for solving transport equations in 1D/2D/3D

    Fosite is a generic framework for the numerical solution of hyperbolic conservation laws in generalized orthogonal coordinates. Its main purpose is the simulation of compressible flows in accretion disks. The underlying numerical solution method belongs to the family of unsplit conservative finite volume TVD schemes. The method is 2nd order accurate in space and uses high order Runge-Kutta and multistep schemes for time evolution. In addition to the pure advection code several source terms...
    Downloads: 1 This Week
    Last Update:
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  • 2

    EmulMultiFit

    Simultaneously fit SAS data with polydisperse core-shell-shell spheres

    Keywords: -simultaneously fit several SAXS and SANS data sets with polydisperse (Schultz-Zimm or Gaussian distribution f(R)) spherical core-shell-shell nanoparticles -analytical expressions are used for from factor F(Q) and its integral over f(R), no numerical integration required -absolute units -Mathematica is required via console (MathKernel) -Mathematica's local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters are possible -Monodisperse(!) hard sphere structure factor can be used, too -long computation times (depending on problem size and amount of constraints) from hours to a few days are possible -non-parallelized code
    Downloads: 0 This Week
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  • 3

    XNDiff

    X-ray and Neutron powder pattern simulation analysis

    Keywords (XNDiff): -SAXS -SANS -absolute units -core (double)shell crystalline nanoparticles -with a parallelepidal shape -particle assemblies -powder and ensemble average -C/C++ -Unix -OpenMP -HPC Cluster Keywords (BatchMultiFit): -simultaneous fits for several SAXS and SANS curves with simulation data from XNDiff -SANS data can be smeared with dq values from experimental data sets or analytical functions -Mathematica console -local and global optimizers (simulated annealing, differential evolution, Nelder-Mead, ...) can be used -range for fit parameters and further constraints between fit parameters -parallelized (typ. 4-8 threads) TODO (BatchMultiFit): -read and use errorbars from experimental data sets -allow different q-ranges for different data sets in the fits -rewrite and test in Python using e.g. the lmfit module: https://pypi.python.org/pypi/lmfit/ to get rid of Mathematica and to run it on HPC clusters
    Downloads: 0 This Week
    Last Update:
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