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. ⚠️ ...

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

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, ...

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 ...

Memory-based questions serve as a useful tool for analyzing GATE 2025’s subject-wise trends and question patterns. Today GATE exam is scheduled for subjects like Computer Science & Information ...

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 ...

Cycling of co-substrates, whereby a metabolite is converted among alternate forms via different reactions, is ubiquitous in metabolism. Several cycled co-substrates are well known as energy and ...

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.