Labs for the Foundations of Applied Mathematics curriculum.

Numerical computing in Swift – for Linux and macOS

Level Set Method Library

Main repository for sharing files and documents about OpenDreamKit

ArtraCFD: A Computational Fluid Dynamics Solver

Discretizations of Exterior Calculus for Analysis, Geometry and Topology

An implementation of improved incremental Singular Value Decomposition(iSVD) algorithm

A simple Fortran program of Discontinuous Galerkin method(no Limiter now) solving 2D Euler Equation with the Isentropic Vortex initial value.

Overlapping finite element meshes in AMORE

Cortix is a Python library for network dynamics modeling and HPC simulation.

Automatic Differentiation Library

A curated list of resources for mathematics, including theory, proofs, computational tools, datasets, open-source software, and learning materials.

High-performance integer factorization suite implementing GNFS, MPQS, and QS algorithms with optimized lattice reduction, vectorization, GPU acceleration, and tensor-based linear algebra. Features automatic algorithm selection, NUMA-aware scheduling, and checkpoint/restore for computational number theory research and cryptanalytic analysis.

A curated list of tools, frameworks, libraries, and educational resources for computational mathematics.

The W(3,3)-E6 Correspondence Theorem: deriving the Standard Model from a single finite geometry with zero free parameters

fast solver for symmetric positive definite toeplitz system with preconditioned conjugate gradient method.

High-performance matrix manipulation engine. Implementing fundamental linear algebra algorithms and computational logic for data processing and mathematical optimization.

A computational engine for linear algebra operations, implementing matrix arithmetic from scratch. Demonstrating algorithmic logic, efficient mathematical computing, and clean code structures.

Hardware Accelerated General Purpose Mathematics Package (very stale)

Introduction to Data Science: A Computational, Mathematical and Statistical Approach, is Course 1MS041 at Uppsala University, Sweden. It is an introductory first semester course for the Masters Programme in Data Science.