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 ...

Probdiffeq implements adaptive probabilistic numerical solvers for differential equations (ODEs). It builds on JAX, thus inheriting automatic differentiation, vectorisation, and GPU acceleration. ⚠️ ...

In this tutorial, I demonstrate how to solve boundary value differential equations using the Finite Difference Method in Python. Learn step-by-step how to implement this numerical technique, ...

FUnTiDES (Fast Unstructured Time Dynamic Equation Solver) is a high-performance computational library designed to simulate 2D and 3D wave propagation. By solving acoustic and elastic wave equations ...

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 ...

Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods ...

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 ...