Speaker
Description
Physics-Informed Neural Networks (PINNs) aim to solve ordinary differential equations (ODEs) and partial differential equations (PDEs). So far, classical digital NNs have been successfully applied to many PDEs, such as the heat equation, Poisson equation, Navier-Stokes equation, to name a few. Recently, Continuous Variable Quantum Neural Networks (CVQNNs) have also been used to solve PDEs in the context of Quantum Physics-Informed Neural Networks (QPINNs). However, to the best of our knowledge, Classical Photonic networks have not been used for these kinds of tasks (usually various classification tasks are tackled with success). In this study, we plan to compare the method and effectiveness of solving a specific nonlinear PDE, the Burgers equation, in the case of the three networks mentioned above.
