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Csharpnumerics

CsharpNumerics is a lightweight yet powerful .NET library for scientific computing, machine learning, physics, and simulation engines — built entirely in pure C#.

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

🔢 Numerical Analysis​

Linear algebra, transforms, ordinary differential equations (ODEs), vector fields, numerical integration, and advanced computational methods.

📊 Statistics​

Descriptive and inferential statistics, probability distributions, stochastic processes, and Monte Carlo simulation.

🤖 Machine Learning​

Supervised and unsupervised learning, automated model selection, cross-validation, clustering, and model stability analysis.

⚛️ Physics​

Classical mechanics, electromagnetics, heat transfer, environmental modeling, orbital mechanics, oscillations, and astronomy.

⚙️ Simulation Engines​

Modular computational engines designed for integration with external visualization, simulation, and analysis platforms.

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

🚀 Simulation Platform Roadmap​

💻 Phase 1 — Classical Quantum Simulation (Educational)​

Goal: Create CPU-based educational quantum circuit simulators.

Core focus: understanding quantum algorithms and state evolution.

Features

  • Quantum circuit simulation
  • Quantum algorithms
  • Noise modeling
  • Hybrid classical–quantum experimentation
  • State vector simulations
  • Circuit visualization tools

Visualization:

  • quantum circuit diagrams
  • interactive state evolution
  • qubit representation using the Bloch sphere

No specialized hardware required.

Potential uses:

  • education
  • algorithm exploration
  • research prototyping

🧩 Phase 2 — Multiphysics Engine​

Goal: Build a general-purpose physics simulation framework.

Core focus: classical physics and coupled simulations.

Supported physics models

Thermal simulations:

  • heat diffusion
  • thermal propagation in solids Based on the Heat Equation

Fluid simulations:

  • fluid flow
  • pipe flow
  • atmospheric flows Based on the Navier–Stokes equations

Electromagnetism:

  • current in conductors
  • electromagnetic wave propagation Based on Maxwell's equations

Example applications:

  • heat propagation in rods
  • fluid flow through pipes
  • electromagnetic effects in conductors
  • environmental physics models

🪐 Phase 3 — Astronomy & Exoplanet Simulation​

Goal: Build an astronomy simulation and discovery platform combining machine learning, numerical methods, and visualization.

Core focus: exoplanet detection, orbital mechanics, and planetary system simulations.

Supported astronomy models

Orbital mechanics

planetary orbits multi-body gravitational systems planetary system stability simulations based on Newtonian gravity

Exoplanet detection

transit light curve analysis supervised machine learning models AutoGrid model selection with cross validation CNN support for time-series transit detection

Astronomical data sources

datasets from

  • Kepler Space Telescope
  • Transiting Exoplanet Survey Satellite
  • Planet Hunters

Visualization

interactive light curve plots 3D planetary system visualization using Three.js

Example applications

exoplanet candidate detection planetary system simulation machine learning experiments on astronomical datasets educational astronomy simulations