A comprehensive scientific computing library implementing physics simulations, stochastic processes, and numerical methods in Python.
SciForge provides a robust set of tools for scientific computing, with a focus on physics calculations, stochastic process simulations, and numerical analysis. Built on NumPy, it offers efficient implementations of common algorithms and models.
- Special relativity calculations including:
- Length contraction and time dilation
- Relativistic mass, energy and momentum
- Lorentz transformations and gamma factor
- Classical field calculations:
- Electric fields and scalar potentials from point charges
- Magnetic fields and vector potentials from current elements
- Gravitational fields and potentials from point masses
- Field visualization tools
- Discrete event simulation:
- Poisson process with configurable rates
- Continuous-time processes:
- Wiener process (Brownian motion)
- Ornstein-Uhlenbeck mean-reverting process
- Geometric Brownian Motion with drift
- Cox-Ingersoll-Ross square-root diffusion
- Vasicek interest rate model
- Statistical analysis tools
- Integration techniques:
- Trapezoid rule for smooth functions
- Simpson's rule for higher accuracy
- Adaptive step size methods
- Error estimation and convergence analysis