TensorMesh is a finite element method (FEM) library built natively on PyTorch. It is designed to solve partial differential equations (PDEs) with the ergonomics of modern deep learning frameworks — ...

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, ...

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 ...