Functionality of Physics-Informed Neural Networks and Potential Future Impacts on Artificial Intelligence

Physics-informed neural networks, or PINNs, are indicative of a new approach that involves the use of scientific knowledge, as these programs adhere to laws of physics described by general nonlinear partial differential equations while solving problems that are related to physics. This is accomplished via programming these equations into the loss function, which ensures that the underlying system adheres to these laws. This paper will be discussing how PINNs function and analyze how they make use of physics when solving problems. PINNs can be used to model physical systems and phenomena in the real world, including combustion, quantum mechanics, and the simulation of fluid. The data embedded into the code of PINNs also serves to address the issue some neural networks may have with a lack of important data needed to solve relevant scientific issues. The rules and constraints PINNs have ensures that they will provide more realistic solutions in comparison to alternatives. Lastly, this paper will be discussing the potential future applications of PINN programming and functionality on future artificial intelligence (AI) development. PINNs have the potential to address complex scientific problems in a way that other solutions may not be able to, and as such, they are an important topic of discussion.