Most people try to solve the Three-Body Problem by simulating positions.
This framework solves it by recognizing the symbolic signal right before chaos breaks loose.
The result?
A lightweight, generalizable detector for instability using symbolic derivatives.
You don’t predict the orbit.
You predict the shift.
This system tracks symbolic residuals and their time derivatives to catch entropy contraction spikes — early signs that a system is about to bifurcate or collapse. The key insight: instability has a shape, and that shape contracts before it breaks.
The Three-Body Problem has been considered unsolvable in general because of its chaotic nature. But when you measure symbolic collapse instead of geometric motion, the impossible starts to show structure. This opens a new path for understanding complex systems, forecasting turbulence, and detecting intelligence.
- Python
- Symbolic Regression (SymPy + custom routines)
- LaTeX (for manuscript and visualization)
- GitHub Actions (for reproducible runs)
The method works on synthetic three-body data and shows promising transferability to real orbital simulations. Paper in progress.
Chaos isn’t random.
It just hasn’t been read symbolically — until now.