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cerx-ML - Cost-Effective Rocketry Experiments with Machine Learning

Overview

Welcome to cerx-ML, a project aimed at minimizing costs for rocketry experiments, particularly focusing on sounding rockets, through the application of machine learning techniques. The project leverages the power of Intel extensions, along with oneAPI optimized machine learning libraries for enhanced performance across a diverse range of hardware accelerators.

Project Goals

  • Cost Reduction - The primary goal of cerx-ML is to explore and implement machine learning algorithms that contribute to cost-effective rocketry experiments, especially those involving sounding rockets.

  • Optimization with oneAPI - The project is powered by oneAPI, a cross-architecture programming model designed to deliver high performance computing across various hardware platforms. The use of oneAPI ensures efficient utilization of computational resources.

  • Intel Extensions - Intel extensions are incorporated to further enhance the performance of machine learning libraries. This optimization is crucial for achieving reliable and rapid results in the field of rocketry.

Usage

To use cerx-ML, follow these steps:

  1. Clone the repository:

    git clone https://github.com/mdjannatulnayem/cerx-ml.git

  2. Locate the det_classification.py file and run it using the following command:

    python det_classification.py

    You will be prompted to provide the following attributes defined by the crex_utility class in the utility.py file:

    • ed -> engine diameter (float)
    • nd -> nozzle diameter (float)
    • l -> engine length (float)
    • mat_S -> material strength (float)
    • fuel -> fuel type (int)
    • ewt -> empty weight (float)
    • lwt -> loaded weight (float)

    The variable det (detonate or not) will be predicted by classification algorithms, and max_t (max thrust produced by the engine) will be predicted by a regression algorithm.

  3. Locate the force_regression.py file and run it using the following command:

    python force_regression.py

    Again, you will be prompted to provide the necessary attributes to determine the maximum amount of thrust to be produced by the engine.

Note: All attributes should be non-negative.

Feel free to explore and contribute to the cerx-ML project!