kimy-de/pinns
Physics-informed neural networks (PINNs)
This project provides a way to solve complex physics equations without needing a lot of experimental data. By inputting the initial and boundary conditions of a system, it uses a type of neural network to predict the system's behavior over time. This is useful for researchers and engineers who need to model physical phenomena like fluid dynamics or phase transitions when observational data is scarce.
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Use this if you need to simulate continuous physical processes and have limited real-world observation data, relying mostly on initial and boundary conditions.
Not ideal if you primarily work with discrete systems or have abundant reference data for traditional data-driven modeling approaches.
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Jupyter Notebook
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Last pushed
Jun 07, 2022
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