I’m a mechanical engineering student, I need to use ML to help me to simulate physical and natural process like “Fluid Flow & Heat Transfer” which is application of PDEs solved by PINNs, so I need a learning path to achieve this level, I’m good enough in prerequires of ML and now I’m talking the Machine Learning Specialization
A while ago I was participating in a research project that involved PINN’s. Here are the main references I used:
- Physics Informed Deep Learning. This ref is good to introduce PINN’s.
Integrating Physics-Based Modeling With Machine
Learning: A Survey. This reference gives an overview of the possibilities of integrating physics with Machine Learning.
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
- Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations
I used the articles written by Professor M. Raissi a lot, as it has code available on github (GitHub - maziarraissi/PINNs: Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations).
I am so very grateful for your time, Mr. Wesley, I will check them all
I have been researching the equation of heat transfer with the help of PINN’s for a year, but I did not get a good result. Thank you for helping me if you have succeeded in solving the equation.