Showing 6 open source projects for "parametric"

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  • 1
    jEPlus - Parametrics for E+ and TRNSYS

    jEPlus - Parametrics for E+ and TRNSYS

    An EnergyPlus shell for parametric studies and more ...

    Parametric analysis is often needed for exploring design options, especially when a global optimization method is not available, or the optimization result is in doubt. Parametric analysis can also be applied to all design variables simultaneously, which forms an exhaustive search that, providing that the search grid is fine enough, will guarantee a global optimum solution.
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    Downloads: 11 This Week
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  • 2
    LabRPS

    LabRPS

    Random phenomena generator

    This is an official mirror of LabRPS. Code and release files are primarily hosted on https://github.com/LabRPS/LabRPS and mirrored here LabRPS aims to be a tool for the numerical simulation of random phenomena such as stochastic wind velocity, seismic ground motion, sea surface ... etc. It can be in a wide range of uses around engineering, such as random vibration or vibration fatigue in mechanical engineering, buffeting analysis in bridge engineering.... LabRPS is mainly to assist...
    Downloads: 3 This Week
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  • 3
    IMEP-Spice interface
    The developed graphical interface is based on the SKILL language which is a Lisp dialect used as a scripting language and PCell (Parameterized Cells) description language used in many EDA software suites by Cadence Design Systems.
    Downloads: 0 This Week
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  • 4
    The C++ library Geneva allows to run large scale parametric optimization problems. It can run in serial or multi-threaded mode or in a networked environment. The library currently covers Evolutionary Strategies, Genetic Algorithms and mixed scenarios.
    Downloads: 0 This Week
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  • 5
    A parametric, feature-based, sketch-driven 3D CAD application, built around a geometrical constraint solving engine. Project is in early development stage. The FreeSG library (http://freesg.org/) is required.
    Downloads: 21 This Week
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  • 6
    ...Its inferential capabilities can be considered surpassed (at best). Actually the parameter estimation methods were already far from the state-of-art when the project began in 2007 (!). The considered implemented parametric and non-parametric Monte Carlo likelihood methods were chosen for their ability to treat both one-dimensional and multivariate SDE systems, although the quality of the inferential results can't match those obtained using more advanced techniques. Nevertheless the toolbox capabilities to simulate numerical solutions of SDE systems are still valid and can serve as a useful starting point to those willing to simulate stochastic dynamical models easily.
    Downloads: 0 This Week
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