Package for the generation of coefficients used in Spectral Deferred Correction and related methods (Runge-Kutta, ...)
Modular simulation framework for discrete and differential temporal simulations with integrated Monte Carlo capabilities.
Exported low-stage, high-order and very-high-order explicit Runge-Kutta tableaux for smooth, nonstiff ODEs.
Runge–Kutta Physics-Informed Neural Networks with time-discrete losses (Gauss/Radau/Lobatto) in PyTorch.
This repository contains a robust and flexible Python package for solving ordinary differential equations (ODEs). It supports both Runge–Kutta schemes (explicit, DIRK) and BDF multistep methods, with adaptive time-stepping and strong support for sparse Jacobians--making it well-suited for large-scale and stiff problems.
Playground to investigate and prototype time-integration methods with qmat
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