AryaAftab/Physics-based-neural-network
Physics-based neural network with Sine Activation Function
This project helps scientists and engineers approximate solutions to complex partial differential equations (PDEs) that describe physical phenomena, like fluid flow or heat transfer. You input the PDE and its boundary conditions, and it outputs a learned function that approximates the true solution, without needing a traditional grid-based computational mesh. Researchers in computational physics, engineering, or applied mathematics would find this useful.
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Use this if you need to find numerical solutions to PDEs and want to explore modern, computationally efficient methods that avoid meshing complexities.
Not ideal if you prefer established, grid-based finite element or finite difference methods, or if your PDEs are simple enough for analytical solutions.
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Aug 28, 2021
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