kochlisGit/Physics-Informed-Neural-Network-PINN-Tensorflow
Implementation of a Physics Informed Neural Network (PINN) written in Tensorflow v2, which is capable of solving Partial Differential Equations.
This project helps scientists, engineers, and researchers solve complex Partial Differential Equations (PDEs) and Ordinary Differential Equations (ODEs) without needing direct analytical solutions. You input the differential equation's mathematical form and any boundary or initial conditions. The output is a highly accurate, data-driven approximation of the equation's solution. It's designed for professionals working with physical models or simulations.
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Use this if you need to find numerical solutions to differential equations quickly and accurately, especially when traditional methods are too slow or complex.
Not ideal if you already have a comprehensive dataset of inputs and outputs for your problem, as this method excels when the solution itself is unknown but the governing equations are.
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Apr 26, 2022
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