A physics-to-code portfolio that couples symbolic calculus with numerical discretization to analyze complex multibody kinematic systems — going from a derived equation of motion to a working, testable simulation.
A physics simulation engine combining SymPy algebraic differentiation with NumPy finite differences to map the trajectory, velocity error, and Coriolis acceleration of an expanding, rotating single-link manipulator.
A computational tracking utility that processes discrete positional datasets, filters structural signal noise, and reconstructs exact motion profiles over time.
A centralized mathematical and algorithmic reference — formula derivations, vector mechanics identities, and their Python implementations for numerical discretization.
- Language: Python
- Symbolic & Numerical Computing: SymPy, NumPy
- Focus areas: solid mechanics, multibody kinematics, numerical methods
Symbolic derivations are exact but static; numerical simulation is dynamic but approximate. This lab is built to bridge the two — deriving the physics by hand, then validating and visualizing it in code.
Developed by Sara Bablal — Energy Engineering Student