Physics, math, and computational experiments revealing hidden structure in the universe — one simulation at a time.
If you can simulate it, you can understand it. These experiments poke at physical reality through the lens of computation — turning equations into interactive visuals and hard problems into things you can drag around with a mouse.
The domains we keep coming back to — each one a rabbit hole with no visible bottom.
Lattice dynamics, phonon dispersion, and how collective vibrations give materials their bulk thermal properties. Emergent behavior in crystalline structures.
Primes, gaps, and the strange geometric patterns hiding inside arithmetic sequences. Simple rules, deep structure — the classic trick of mathematics.
Manifolds, knots, and higher-dimensional objects projected into forms your eyes can process. The hypercube is just the beginning.
Sensitive dependence on initial conditions, strange attractors, Lyapunov exponents. Deterministic systems that look random — because "random" might just mean unpredictable.
Fields, hysteresis loops, and the classical physics of circuits. From logic gate timing to magnetic saturation curves — EM is everywhere and still surprising.
Entropy, mutual information, channel capacity. Shannon turned communication into math. Now that math shows up in thermodynamics, biology, and machine learning.
Three principles that guide how we build experiments here.
A static graph of phonon dispersion is a diagram. A canvas where you can drag the lattice spacing and watch the curves shift in real time is understanding.
Every simulation we build has a moment where the output doesn't match the expectation. That moment — not the initial hypothesis — is usually the interesting part.
When a simulation is simplified or wrong in known ways, say so. The goal isn't to replace a textbook — it's to build intuition that a textbook can't provide.