Physicsl-informed Machine Learning
Physics-informed Machine Learning (PIML)
Solving Differential Equations with Physics-Informed Neural Networks (PINNs): A mild introduction with Pytorch - Souvik
In science and engineering, partial differential equations (PDEs) are foundational tools used to describe a wide range of natural phenomena — from the vibrations of a guitar string to the diffusion of heat in a metal rod or the flow of fluids in porous media. Traditionally, solving these equations has required numerical methods such as finite difference or finite element techniques, which discretize the space and time domain into meshes and solve iteratively.
But with the rise of machine learning, a new idea emerged: what if we could use artificial neural networks (ANNs) to learn the solution of a PDE directly?
https://medium.com/@souvikat/solving-differential-equations-with-physics-informed-neural-networks-pinns-a-mild-introduction-5570634149b8
