Showing 12 open source projects for "neural python"

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  • 1
    GNNePCSAFT

    GNNePCSAFT

    Smart Thermodynamic Modeling with Graph Neural Networks

    Our project harnesses the power of Graph Neural Network (GNN) to estimate pure-component parameters of the state-of-the-art Equation of State, PC-SAFT. We aim to empower users to leverage this robust equation without the need for prior experimental data, revolutionizing the calculation of thermodynamic properties and enhancing process simulations. FeOS is used for the PC-SAFT calculations. The estimated parameters can be used in DWSIM and Aspen HYSYS process simulators.
    Downloads: 1 This Week
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  • 2
    Kinetic.jl

    Kinetic.jl

    Universal modeling and simulation of fluid mechanics upon ML

    Kinetic is a computational fluid dynamics toolbox written in Julia. It aims to furnish efficient modeling and simulation methodologies for fluid dynamics, augmented by the power of machine learning. Based on differentiable programming, mechanical and neural network models are fused and solved in a unified framework. Simultaneous 1-3 dimensional numerical simulations can be performed on CPUs and GPUs.
    Downloads: 0 This Week
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  • 3

    Moose

    Multiscale Neuroscience and Systems Biology Simulator

    Moose is the core of a modern software platform for the simulation of neural systems ranging from subcellular components and biochemical reactions to complex models of single neurons, large networks, and systems-level processes. We have moved Github.com. This should be your source for the latest version of the code.
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    Downloads: 1 This Week
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  • 4

    nevesim

    NEVESIM is an event-driven neural simulation tool.

    NEVESIM is a software package for event-driven simulation of networks of spiking neurons with a fast simulation core in C++, and a scripting user interface in the Python programming language. It supports simulation of heterogeneous networks with different types of neurons and synapses, and can be easily extended by the user with new neuron and synapse types. To enable heterogeneous networks and extensibility, NEVESIM is designed to decouple the simulation logic of communicating events...
    Downloads: 0 This Week
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    Topographica

    Simulation software for brain modelling

    Topographica is a neural modeling package developed in a Human Brain Project grant from NIH. Topographica helps neuroscientists and computational scientists simulate and understand how topographic maps contribute to brain function. Topographica is now primarily maintained at github.com; see https://github.com/ioam/topographica for recent updates and releases.
    Downloads: 1 This Week
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  • 6
    NeMo is a high-performance spiking neural network simulator which simulates networks of Izhikevich neurons on CUDA-enabled GPUs. NeMo is a C++ class library, with additional interfaces for pure C, Python, and Matlab.
    Downloads: 0 This Week
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  • 7
    PCSIM is a tool for distributed simulation of heterogeneous networks composed of different model neurons and synapses. The development of PCSIM was supported by the FACETS EU project.
    Downloads: 0 This Week
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  • 8
    Brainlab is a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator (NCS).
    Downloads: 0 This Week
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  • 9
    Brian is a new simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists.
    Downloads: 0 This Week
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  • 10
    We experiment with Evolution of Artifical Neural Networks, combining the two fields of Evolutionary Computation and ANNs. Our methods are applied to a variety of interesting problems. To learn more, click on "Home Page", "Mail", or "Files".
    Downloads: 2 This Week
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  • 11
    Simulator of virtual animals made up of biological neural networks for research in the Computational Neuroscience field.
    Downloads: 0 This Week
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  • 12
    a distributed engine for abstract neural network development via natural-language programming
    Downloads: 0 This Week
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