kanhaiya-gupta/physics-informed-neural-network
A Physics-Informed Neural Network (PINN) framework for solving partial differential equations (PDEs) with FastAPI integration. This project implements PINNs for various physical systems including simple harmonic motion, heat transfer, wave propagation, and fluid dynamics. The framework provides a modular architecture for training.
This framework helps scientists and engineers solve complex physics problems described by partial differential equations (PDEs), such as heat transfer or wave propagation. You provide the physical equation, and the system trains a neural network to find its solution, even without extensive measurement data. The output is a predictive model that respects the underlying physics, along with visualizations of the solution and training metrics.
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Use this if you need to model physical phenomena like simple harmonic motion, heat distribution, wave propagation, or fluid dynamics (Burgers' equation) and want to leverage neural networks informed by physical laws.
Not ideal if your problem does not involve partial differential equations or if you require purely data-driven models without enforcing physical constraints.
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Apr 18, 2025
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