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 ...

We present NeuralMag, a flexible and high-performance open-source Python library for micromagnetic simulations. NeuralMag leverages modern machine learning frameworks, such as PyTorch and JAX, to ...

When it comes to quant finance, few tasks evoke as much existential dread in quants as calculating sensitivities for the purposes of FRTB or XVA. These sensitivities that tell risk managers how jumpy ...

The numerical solution of partial differential equations (PDEs) is essential in computational physics. Over the past few decades, various quantum-based methods have been developed to formulate and ...

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 ...