nanditadoloi/PINN
Simple PyTorch Implementation of Physics Informed Neural Network (PINN)
This tool helps scientists and engineers solve complex physics problems, like understanding heat flow, by integrating known physical laws directly into a machine learning model. You provide the governing differential equations and boundary conditions, and it outputs a model that approximates the solution, even for challenging scenarios like fluid flow through porous media. This is ideal for researchers in fields like geology, material science, or fluid dynamics.
369 stars. No commits in the last 6 months.
Use this if you need to find numerical solutions to partial differential equations while ensuring the solutions adhere to physical principles, especially when traditional numerical methods are too slow or complex.
Not ideal if your problem doesn't involve physical laws expressible as differential equations or if you're primarily focused on data-driven prediction without physical constraints.
Stars
369
Forks
57
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 05, 2024
Commits (30d)
0
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