filippo-masi/Thermodynamics-Neural-Networks
Thermodynamics-based Artificial Neural Networks
This project offers a new way to create predictive models for how materials behave under stress, like elasto-plastic materials. It takes material behavior data and outputs a thermodynamically consistent model that predicts stress-strain relationships. Materials scientists, mechanical engineers, and researchers working on advanced material design would find this useful for developing robust constitutive models.
No commits in the last 6 months.
Use this if you need to model the mechanical behavior of materials and ensure your predictions remain physically consistent with the laws of thermodynamics, even with limited experimental data.
Not ideal if you are looking for a general-purpose neural network framework without a specific focus on physics-based material modeling.
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32
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8
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Apr 05, 2023
Commits (30d)
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