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Charpnumerics

CsharpNumerics is a lightweight yet powerful .NET library for numerical computing, machine learning, statistics, and physics simulations — built entirely in pure C#.

It provides a clean, modular API with robust building blocks for scientific and engineering workflows, including:

  • Numerical Analysis — linear algebra, transforms, ordinary differential equations (ODEs), vector fields, and numerical integration methods
  • Machine Learning — regression, classification, cross-validation, and reusable pipelines
  • Statistics — descriptive analytics, probability tools, and interpolation techniques
  • Physics — classical mechanics, rigid body dynamics, orbital mechanics, and real-time simulation

📦 Available on NuGet: https://www.nuget.org/packages/CSharpNumerics/

🚀 Future Directions — Roadmap​

CsharpNumerics is evolving into a comprehensive scientific computing and simulation framework, bridging applied mathematics, physics-based modeling, machine learning, and real-time numerical systems.

🧩 Phase 1 — Stochastic Foundations & Solvers​

Goal: Extend the Statistics module to provide broader support for simulation and probabilistic calculations.

  • Features:
    • Implementation of probability density functions (Normal, Poisson, Beta, Gamma).
    • Monte Carlo Simulation – A multi-threaded framework for running independent simulations to quantify risk and probability.
    • Utilities for uncertainty quantification and resampling

🎧 Phase 2 — Audio & Oscillation Engine​

Goal: Build a concrete application of the Oscillations module.

  • Features:

    • Wave propagation, Fourier transforms, filters
    • Feature extraction on signals using ML
    • Real-time or offline simulation pipelines

🌍 Phase 3 — GeoEngine & Spatial Simulations​

Goal: Apply statistical modeling and Monte Carlo methods to spatial problems.

  • Features:

    • Raster- and mesh-based spatial representation
    • Monte Carlo pipelines for probability and risk maps
    • Visualization: probability fields, percentiles, extreme scenarios
    • Spatial Clustering: Identifying "hotspots" or risk zones using unsupervised learning.

💻 Phase 4 — Classical Quantum Simulation (Educational)​

Goal: CPU-based quantum circuit simulators

  • Features:

    • Quantum algorithms
    • Noise models
    • Hybrid classical–quantum experimentation

(No specialized hardware required)