Showing 4 open source projects for "artificial intelligence python"

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  • 1
    GNNPCSAFT Chat

    GNNPCSAFT Chat

    Chatbot with GNNPCSAFT

    The GNNPCSAFT Chat is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, you can chat with LLM models (Gemini or Ollama) with GNNPCSAFT tools, allowing you to ask questions about the PC-SAFT parameters of various compounds, predict thermodynamic properties, and get insights into the...
    Downloads: 1 This Week
    Last Update:
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  • 2
    GNNPCSAFT Web App

    GNNPCSAFT Web App

    Smart Thermodynamic Modeling with Graph Neural Networks

    The GNNPCSAFT Web App is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. More info on github repository.
    Downloads: 2 This Week
    Last Update:
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  • 3

    openhealthpractice

    A simulation environment for physiology models simulation

    The OpenHealhPractice Simulation Environment is a web framework intended to concurrently manage and launch generalized models. Models can be uploaded by the users and also can be connected to other models in order to simulate them in a row. These simulations are stored and can be reviewed or deleted at anytime. The Graphical Visualization Environment (GVE) provides the graphical visualization of the simulation results. Results can also be exported to different formats (xls, txt or...
    Downloads: 0 This Week
    Last Update:
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  • 4
    This project intends to create a bacteria simulator framework, with some realistic bacteria control methods based on chemical signaling, simple sensors, motors and neural networks. The bacteria will evolve in a genetic algorithm environment.
    Downloads: 0 This Week
    Last Update:
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