Abstract: Partial differential equations (PDEs) are ubiquitous to the mathematical description of physical phenomena. Typical examples describe the evolution of a field in time as a function of its ...

high-order finite-difference solvers for dataset generation, the Burgers-equation PhyCRNet model implementation, a training and evaluation entrypoint, utility functions for checkpointing, plotting, ...

This set of tutorials are written at an introductory level for an engineering or physical sciences major. It is ideal for someone who has completed college level courses in linear algebra, calculus ...

Solving partial differential equations is computationally expensive, creating challenges for real-time physics simulations involving the wave equation in virtual acoustics—e.g., mixed reality, spatial ...

Physics-Informed Neural Networks (PINN) emerged as a powerful tool for solving scientific computing problems, ranging from the solution of Partial Differential Equations to data assimilation tasks.

Physical scientists and engineering research and development (R&D) teams are embracing neural networks in attempts to accelerate their simulations. From quantum mechanics to the prediction of blood ...

Physics-Informed Neural Networks (PINN) are neural networks encoding the problem governing equations, such as Partial Differential Equations (PDE), as a part of the neural network. PINNs have emerged ...