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        "author": "Felipe Viana, Renato G. Nascimento, Yigit Yucesan, Arinan Dourado",
        "author_email": "viana@ucf.edu, renato.gn@knights.ucf.edu, yucesan@knights.ucf.edu, arinandourado@knights.ucf.edu",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Topic :: Education",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Artificial Intelligence",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "[![PyPI version](https://badge.fury.io/py/pml-pinn.svg)](https://badge.fury.io/py/pml-pinn)\n# Physics-informed neural networks\nWelcome to the PML repository for physics-informed neural networks. We will use this repository to disseminate our research in this exciting topic. Links for some useful publications:\n* [**Fleet Prognosis with Physics-informed Recurrent Neural Networks:**](https://arxiv.org/abs/1901.05512) This paper introduces a novel physics-informed neural network approach to prognosis by extending recurrent neural networks to cumulative damage models. We propose a new recurrent neural network cell designed to merge physics-informed and data-driven layers. With that, engineers and scientists have the chance to use physics-informed layers to model parts that are well understood (e.g., fatigue crack growth) and use data-driven layers to model parts that are poorly characterized (e.g., internal loads).\n\n## Install\n\nTo install the stable version just do:\n```\npip install pml-pinn\n```\n\n### Develop mode\n\nTo install in develop mode, clone this repository and do a pip install:\n```\ngit clone https://github.com/PML-UCF/pinn.git\ncd pinn\npip install -e .\n```\n\n\n",
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        "summary": "Physics-informed neural networks",
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