High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
-
Updated
May 16, 2024 - Julia
High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.
A Comprehensive Julia implementation of the Vortex Lattice Method
Solvers for steady states in scientific machine learning (SciML)
Add a description, image, and links to the steady-state topic page so that developers can more easily learn about it.
To associate your repository with the steady-state topic, visit your repo's landing page and select "manage topics."